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[[https://www.packtpub.com/big-data-and-business-intelligence/next-generation-natural-language-processing-python-video|Next Generation Natural Language Processing with Python {Video}]]

Alexis Rutherford

ISBN 13: 9781789139938 Packt Course Length: 1 hour 56 minutes (MARCH 2018)

'''Video Overview:'''

Practical techniques and methods to analyze your text data. Power your decision making using next generation libraries.

The company you work for has accumulated a lot of valuable data from its customers, all stored as text, and you need to extract some value from that data. You’ve spent a lot of combined time writing about what they want but no-one knows what they have written about and no-one has the time to read all the messages.

This course empowers you to know how to attack this and other text analysis problems to unlock value for your organization. You’ll start by seeing how NLP can help you extract useful information from large collections of text data, and how you can use the latest Python libraries for NLP. Then we’ll show you how to solve a practical problem using NLP by building a spam SMS detector. You’ll also learn to convert words into numbers that can be analyzed.

[[https://www.packtpub.com/big-data-and-business-intelligence/next-generation-natural-language-processing-python-video|Publisher's page]]

----

Next Generation Natural Language Processing with Python {Video}

Alexis Rutherford

ISBN 13: 9781789139938 Packt Course Length: 1 hour 56 minutes (MARCH 2018)

Video Overview:

Practical techniques and methods to analyze your text data. Power your decision making using next generation libraries.

The company you work for has accumulated a lot of valuable data from its customers, all stored as text, and you need to extract some value from that data. You’ve spent a lot of combined time writing about what they want but no-one knows what they have written about and no-one has the time to read all the messages.

This course empowers you to know how to attack this and other text analysis problems to unlock value for your organization. You’ll start by seeing how NLP can help you extract useful information from large collections of text data, and how you can use the latest Python libraries for NLP. Then we’ll show you how to solve a practical problem using NLP by building a spam SMS detector. You’ll also learn to convert words into numbers that can be analyzed.

Publisher's page


Learn Machine Learning in 3 Hours (Video)

Thomas Snell

ISBN 13: 9781788995580 Course Length: 2 hours 14 minutes (March 2018)

Video Overview:

Get hands-on with machine learning using Python

Given the constantly increasing amounts of data they're faced with, programmers have to come up with better solutions to make machines smarter and reduce manual work. In this Machine Learning course, you'll use Python to craft better solutions and process them effectively.

By the end of the course, you will be adept at using the concepts and algorithms involved in Machine Learning. This is a highly practical course and will equip you with sufficient hands-on training to help you implement ML skills right after finishing the course.

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Advanced Artificial Intelligence Projects with Python

Joshua Eckroth

ISBN 13: 9781788832403 Packt Course Length: 2 hours 02 minutes (March 2018)

Video Overview:

Enter and explore the fascinating world of intelligent applications with Artificial Intelligence using the Python programming language

Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow.

This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations.

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Create Your Own Sophisticated Model with Neural Networks (Video)

Julian Avila

ISBN 13: 9781789130157 Packt Course Length: 1 hour and 24 minutes (March 2018)

Video Overview:

AA one-stop solution to learning complex models with Neural Networks and understanding the basics of Natural Language Processing.

With this course you will learn the Decision Tree algorithms and Ensemble Models to build Random Forest, Regression Analysis. You will focus on Decision Trees and Ensemble Algorithms. Moving forward, you learn to use scikit-learn to classify text and Multiclass with scikit-learn. You will explore various algorithms for classification. You will also look at Naive Bayes model and Label Propagation. Finally, you'll use Neural Networks using different Classifiers and create your own Simple Estimator.

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Hands-on Artificial Intelligence with TensorFlow (Video)

Saikat Basak

ISBN 13: 9781789135091 Packt Course Length: 1 hour and 36 minutes (March 2018)

Video Overview:

A practical approach to deep learning and deep reinforcement learning for building real-world applications using TensorFlow.

This course will show you how to combine the power of Artificial Intelligence and TensorFlow to develop some exciting applications for the real world. This course will take you through all the relevant AI domains, tools, and algorithms required to build optimal solutions and will show you how to implement them hands-on. You will then be taken through techniques such as reinforcement learning, heuristic searches, neural networks, Computer Vision, OpenAI Gym, and more in different stages of your application. This course will show you how to implement AI practically using TensorFlow models and how it eases the way you interact with the technology.

You will learn how TensorFlow can be used to analyze a variety of data sets and will learn to optimize various AI algorithms. By the end of the course, you will have learned to build intelligent apps by leveraging the full potential of Artificial Intelligence with TensorFlow.

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OpenCV 3 Computer Vision with Python Cookbook

Alexey Spizhevoy, Aleksandr Rybnikov

ISBN 13: 9781788474443 Packt 306 Pages (March 2018)

Book Overview:

Recipe-based approach to tackle the most common problems in Computer Vision by leveraging the functionality of OpenCV using Python APIs

In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We’ll explore techniques to achieve camera calibration and perform a multiple-view analysis.

Later, you’ll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You’ll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks.

By the end of the book, you’ll be able to apply your skills in OpenCV to create computer vision applications in various domains.

Publisher's page


Learning Concurrency in Python (Video)

Elliot Forbes

ISBN 13: 9781789134346 Packt Course Length: 3 hours and 35 minutes (March 2018)

Video Overview:

More than 20 videos to help you master concurrency in Python

This course introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python.

The course will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the video course, you'll have learned techniques to write incredibly efficient concurrent systems that follow best practices.

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Hands-On Machine Learning with Python and Scikit-Learn (Video)

Taylor Smith

ISBN 13: 9781788991056 Packt Course Length: 2 hours 39 minutes (March 2018)

Video Overview:

Understand and implement the best Machine Learning practices with the help of powerful features of Python and scikit-learn

This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python along with the Scikit-Learn library.

We begin our journey by observing the end result of a Machine Learning deployment before moving back to the fundamentals and into exploratory data analysis. Moving on, we learn to develop complex pipelines and techniques for building custom transformer objects for feature extraction, manipulation, and other effective data cleansing techniques. Finally, we discover how to select a model, apply optimal hyper-parameters, and deploy it.

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Practical Computer Vision

Abhinav Dadhich

ISBN 13: 9781788297684 Packt 234 Pages (February 2018)

Book Overview:

A practical guide designed to get you from basics to current state of art in computer vision systems.

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.

With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow.

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Machine Learning with Scikit-learn (Video)

Gavin Hackeling

ISBN 13: 9781789134780 Packt Course Length: 3 hours 21 minutes (February 2018)

Book OR Video Overview:

Learn to implement and evaluate machine learning solutions with scikit-learn

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning, you can automate any analytical model. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It also discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more.

You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. By the end of this course, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

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Data Visualization in Python by Examples (Video)

Harish Kumar Garg

ISBN 13: 9781788838658 Packt Course Length: 1 hours 17 minutes (February 2018)

Video Overview:

Data visualization with matplotlib, ggplot, and seaborn in Python

Data visualization is just a wise investment in your future big-data needs. You will learn how to deploy maps and networks to display geographic and network data. To do this, we will focus on the following very popular libraries in Python: matplotlib, ggplot, seaborn, and plotly.

In this course, you will walk through some of the fundamentals of data visualization, sharing many examples of how to handle different types of data and how best to present your insights. We'll take a look at chart types, such as Matplotlib for visualizing the impact of tornadoes in the US, North Korean nuke tests on global stocks, and analyze forex performances using charts. You will see how ggplot can be used to analyze trends in BRICS economies and crude oil price trends. You will see how to level up your data visualization skills using Python's advanced plotting libraries: matplotlib and Seaborn, and how you can present the data from the most unstable regions in the world through data visualization.

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Python Web Scraping Cookbook

Michael Heydt

ISBN 13: 9781787285217 Packt 364 Pages (February 2018)

Book Overview:

Untangle your web scraping complexities and access web data with ease using Python scripts

Right from extracting data from the websites to writing a sophisticated web crawler, the book's independent recipes will be a godsend on the job. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with AJAX websites, paginated items, and more. You will also learn to tackle problems such as 403 errors, working with proxy, scraping images, LXML, and more.

By the end of this book, you will be able to scrape websites more efficiently and to be able to deploy and operate your scraper in the cloud.

Publisher's page


Working with Big Data in Python (Video)

Alex Rutherford

ISBN 13: 9781788839068 Course Length: 2 hours 41 minutes (February 2018)

Video Overview:

Gain valuable insights from your data by streamlining unstructured data pipelines with Python, Spark, and MongoDB

This course is a comprehensive, practical guide to using MongoDB and Spark in Python, learning how to store and make sense of huge data sets, and performing basic machine learning tasks to make predictions.

MongoDB is one of the most powerful non-relational database systems available offering robust scalability and expressive operations that, when combined with Python data analysis libraries and distributed computing, represent a valuable set of tools for the modern data scientist. NoSQL databases require a new way of thinking about data and scalable queries. Once Mongo queries have been mastered, it is necessary to understand how we can leverage this API in Python's rich analysis and visualization ecosystem. This course will cover how to use MongoDB, particularly if you are used to SQL databases, with a focus on scalability to large datasets. pyMongo is introduced as the means to interact with a MongoDB database from within Python code and the data structures used to do so are explored. MongoDB uniquely allows for complex operations and aggregations to be run within the query itself and we will cover how to use these operators. While MongoDB itself is built for easy scalability across many nodes as datasets grow, Python is not. Therefore, we cover how we can use Spark with MongoDB to handle more complex machine learning techniques for extremely large datasets. This learning will be applied to several examples of real-world datasets and analyses that can form the basis of your own pipelines, allowing you to quickly get up-and-running with a powerful data science toolkit.

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Learning PySpark (Video)

Tomasz Drabas

ISBN 13: 9781788396592 Course Length: 2 hours 29 minutes (February 2018)

Video Overview:

Building and deploying data-intensive applications at scale using Python and Apache Spark

You'll learn about different techniques for collecting data, and distinguish between (and understand) techniques for processing data. Next, we provide an in-depth review of RDDs and contrast them with DataFrames. We provide examples of how to read data from files and from HDFS and how to specify schemas using reflection or programmatically (in the case of DataFrames). The concept of lazy execution is described and we outline various transformations and actions specific to RDDs and DataFrames.

Finally, we show you how to use SQL to interact with DataFrames. By the end of this tutorial, you will have learned how to process data using Spark DataFrames and mastered data collection techniques by distributed data processing.

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Concurrent Programming in Python (Video)

BignumWorks Software LLP

ISBN 13: 9781788998031 Course Length: 2 hours 20 minutes (February 2018)

Video Overview:

Harness the power of modern code structures with Python to improve performance and flexibility

Filled with examples, this course will show you all you need to know to start using concurrency in Python. You will learn about the principal approaches to concurrency that Python has to offer, including libraries and tools needed to exploit the performance of your processor. Learn the basic theory and history of parallelism and choose the best approach when it comes to parallel processing.

After taking this course you will have gained an in-depth knowledge of using threads and processes with the help of real-world examples.

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Concurrent Programming in Python (Video)

BignumWorks Software LLP

ISBN 13: 9781788998031 Course Length: 2 hours 20 minutes (February 2018)

Video Overview:

Harness the power of modern code structures with Python to improve performance and flexibility

Filled with examples, this course will show you all you need to know to start using concurrency in Python. You will learn about the principal approaches to concurrency that Python has to offer, including libraries and tools needed to exploit the performance of your processor. Learn the basic theory and history of parallelism and choose the best approach when it comes to parallel processing.

After taking this course you will have gained an in-depth knowledge of using threads and processes with the help of real-world examples.

Publisher's page


Deep Learning with PyTorch

Vishnu Subramanian

ISBN 13: 9781788624336 Packt 262 Pages (February 2018)

Book Overview:

Build neural network models in text, vision and advanced analytics using PyTorch

This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them.

You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images.By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.

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Python Network Programming (Video)

Eric Chou

ISBN 13: 9781788479387 Course Length: 2 hours and 3 minutes (February 2018)

Video Overview:

Accomplish Network Engineering Tasks with Python

You will learn to create exciting Python apps to automate daily networking tasks such as configuring devices, collecting information about the network, testing by client simulations, or network discovery. This course will help you build some mesmerizing network tools with Python, including a Subnet calculator; configuring multiple network devices concurrently via SSH or Telnet; a DHCP client simulator for testing a DHCP server in the local network; network discovery via SNMP; OS fingerprinting; and network attacks via Scapy. This tutorial uses real-life scenarios and use cases to help you build network automation tools with the amazing Python language. By the end of this course, you will be able to take your Python programming skills to the next level for network automation.

This course provides hands-on, interesting examples with clear and friendly explanations that students can follow along with, covers common mistakes, and provides useful tips and in-the-trenches advice. There is a limited amount of theory; instead, the examples are full of real-world use cases.

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Python Programming Blueprints

Daniel Furtado, Marcus Pennington

ISBN 13: 9781786468161 Packt 456 Pages (February 2018)

Book Overview:

How to build useful, real-world applications in the Python programming language

In this book, we will cover some of the most common tasks that Python developers face on a daily basis, including performance optimization and making web applications more secure. We will familiarize ourselves with the associated software stack and master asynchronous features in Python. We will build a weather application using command-line parsing. We will then move on to create a Spotify remote control where we'll use OAuth and the Spotify Web API. The next project will cover reactive extensions by teaching you how to cast votes on Twitter the Python way. We will also focus on web development by using the famous Django framework to create an online game store. We will then create a web-based messenger using the new Nameko microservice framework.

We will cover topics like authenticating users and, storing messages in Redis. By the end of the book, you will have gained hands-on experience in coding with Python.

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Building Advanced OpenCV3 Projects with Python (Video)

Riaz Munshi

ISBN 13: 9781788394291 Packt Course Length: 3 hours 30 minutes (January 2018)

Video Overview:

Discover how to build advanced OpenCV3 projects with Python

This course features some trending applications of vision and deep learning and will help you master these techniques. You will learn how to retrieve structure from motion (sfm) and you will also see how we can build an application to capture 2D images and join them dynamically to achieve street views by capturing camera projection angles and relative image positions. You will also learn how to track your head in 3D in real-time, and perform facial recognition against a goldenset. You will also build an app to capture facial emotions based on a CovNet.

Next, you'll generate panoramas using image stitching and we extend this concept by generating a map based on the trajectory of ISS. You'll also learn to build an application to capture beautiful panoramas and also achieve AR effects. You then delve into one of the most trending domains of computer vision: autonomous cars. You'll learn about various architectures and develop the skills to detect lanes, and segment and track vehicles in traffic.You will be using Carla, which is a open driving simulator by Intel, for your project to train a car learn how to drive itself using an end-to-end model.

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Deep Learning Essentials

Wei Di, Anurag Bhardwaj, Jianing Wei

ISBN 13: 9781785880360 Packt 284 Pages (Jan 2018)

Book Overview:

Get to grips with the essentials of deep learning by leveraging the power of Python

This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more

This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications

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Training Your Systems with Python Statistical Modeling (Video)

Curtis Miller

ISBN 13: 9781788293402 Packt Course Length: 4 hours 5 minutes (Jan 2018)

Video Overview:

Learn statistical analysis by using various machine learning models

You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics with Pandas. From there, you will be introduced to supervised learning, where you will explore the principles of machine learning and train different machine learning models. Next, you’ll work with binary prediction models, such as data classification using K-nearest neighbors, decision trees, and random forests.

After that, you’ll work with algorithms for regression analysis, and employ different types of regression, such as ridge and lasso regression, and spline interpolation using SciPy. Then, you’ll work on neural networks, train them, and employ regression on neural networks. You’ll be introduced to clustering, and learn to evaluate cluster model results, as well as employ different clustering types such as hierarchical and spectral clustering. Finally, you’ll learn about the dimensionality reduction concepts such as principal component analysis and low dimension representation.

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Effective Prediction with Machine Learning - Second Edition (Video)

Julian Avila

ISBN 13: 9781789132793 Packt Course Length: 1 hour 32 minutes (Jan 2018)

Video Overview:

A one-stop solution to quickly program fast Machine Learning algorithms with NumPy and scikit-learn

This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. As you progress through the sections, you will come across videos that will teach you to implement techniques such as data pre-processing, linear regression, logistic regression, and K-NN. You will also look at Pre-Model and Pre-Processing workflows, to help you choose the right models.

Finally, you'll explore dimensionality reduction with various parameters.

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IPython Interactive Computing and Visualization Cookbook - Second Edition

Cyrille Rossant

ISBN 13: 9781785888632 Packt 548 Pages (Jan 2018)

Book Overview:

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work.

IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.

The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.

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Ensemble Machine Learning

Ankit Dixit

ISBN 13: 9781788297752 Packt 438 Pages (December 2017)

Book Overview:

An effective guide to using ensemble techniques to enhance machine learning models

This book covers different machine learning algorithms that are widely used in the practical world to make predictions and classifications. It addresses different aspects of a prediction framework, such as data pre-processing, model training, validation of the model, and more. You will gain knowledge of different machine learning aspects such as bagging (decision trees and random forests), Boosting (Ada-boost) and stacking (a combination of bagging and boosting algorithms).

Then you’ll learn how to implement them by building ensemble models using TensorFlow and Python libraries such as scikit-learn and NumPy. As machine learning touches almost every field of the digital world, you’ll see how these algorithms can be used in different applications such as computer vision, speech recognition, making recommendations, grouping and document classification, fitting regression on data, and more.

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Connect the Dots: Factor Analysis (Video)

Loonycorn

ISBN 13: 9781788997522 Packt Course Length: 1 hour 43 minutes (December 2017)

Video Overview:

Factor extraction using PCA in Excel, R and Python

Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect. This course will help you understand Factor analysis and it’s link to linear regression.

See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine learning.

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Connect the Dots: Linear and Logistic Regression (Video)

Loonycorn

ISBN 13: 9781788991957 Packt Course Length: 4 hours 45 minutes (December 2017)

Video Overview:

Build robust models in Excel, R and Python

This course will teach you how to build robust linear models and do logistic regression in Excel, R and Python. Let’s parse that. Robust linear models: Linear Regression is a powerful method for quantifying the cause and effect relationships that affect different phenomena in the world around us.

This course will teach you how to build robust linear models that will stand up to scrutiny when you apply them to real world situations. Logistic regression: Logistic regression has many cool applications: analyzing consequences of past events, allocating resources, solving binary classification problems using machine learning and so on. This course will help you understand the intuition behind logistic regression and how to solve it using cookie-cutter techniques. Excel, R and Python: Put what you've learnt into practice. Leverage these powerful analytical tools to build models for stock returns.

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Advanced Statistics for Machine Learning (Video)

Pratap Dangeti

ISBN 13: 9781788994989 Packt Course Length: 2 hours 10 minutes (December 2017)

Video Overview:

Building various machine learning models using Python and R

This video will teach you all it takes to perform the complex statistical computations required for Machine Learning. You will gain information on statistics behind unsupervised learning, reinforcement learning, and more. You'll master real-world examples that discuss the statistical side of Machine Learning.

In this video, you will acquire a deep knowledge of the various models of unsupervised and reinforcement learning, and explore the fundamentals of deep learning with the help of the Keras software. Furthermore, you'll gain an overview of reinforcement learning with the Python programming language.

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Natural Language Processing with Python (Video)

Tyler Edwards

ISBN 13: 9781787286085 Packt Course Length: 1 hour 47 minutes (December 2017)

Video Overview:

Learn and master the NLTK library in Python to create your own NLP apps

You will start off by preparing text for Natural Language Processing by cleaning and simplifying it. Then you will implement more complex algorithms to break this text down and uncover contextual relationships that reveal the meaning and content of the text. You will learn how to tokenize various parts of sentences, and how to analyze them. You will learn about semantic as well as the syntactic analysis of text.

During this course, you will learn how to solve various ambiguities in processing human language. You will also gain experience with NLP using Python and will be introduced to a variety of useful tools in NLTK. Plus, you will have an opportunity to build your first NLP application. By the end of this course, you will have the skills and tools to begin solving problems in the growing field of Latent Semantic Analysis.

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Python: Advanced Predictive Analytics

Ashish Kumar, Joseph Babcock

ISBN 13: 9781788992367 Packt 660 Pages (December 2017)

Book Overview:

Gain practical insights by exploiting data in your business to build advanced predictive modeling applications

This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications.

Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling.

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Practical Time Series Analysis (Video)

Dr. Avishek Pal, Dr. PKS Prakash

ISBN 13: 9781788995719 Packt Course Length: 2 hours 25 minutes (December 2017)

Video Overview:

Step-by-step guide filled with real-world practical examples

The video starts with a descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift the focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting.

Later, powerful deep learning methods are presented to develop accurate forecasting models for complex time series. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.

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SciPy Recipes

L. Felipe Martins, Ruben Oliva Ramos, V Kishore Ayyadevara

ISBN 13: 9781788291460 Packt 386 Pages (December 2017)

Book Overview:

Tackle the most sophisticated problems associated with scientific computing and data manipulation using SciPy

This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems.

The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.

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Advanced Techniques for Exploring Data Sets with Pandas (Video)

Harish Garg

ISBN 13: 9781788397599 Packt Course Length: 1 hours 45 minutes (July 2017)

Video Overview:

Explore popular datasets in R, while mastering advanced techniques used for them

In this course, you will learn how to start using pandas from end-to-end: from getting your data into pandas; using pandas to manipulate, transform, analyze, and visualize data; to ultimately taking your transformed data out of pandas into any number of formats.

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scikit-learn : Machine Learning Simplified

Raúl Garreta, Guillermo Moncecchi, Trent Hauck, Gavin Hackeling

ISBN 13: 9781788833479 Packt 530 Pages (November 2017)

Book Overview:

Implement scikit-learn into every step of the data science pipeline

The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.

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Natural Language Processing with Python Cookbook

Krishna Bhavsar, Naresh Kumar, Pratap Dangeti

ISBN 13: 9781787289321 Packt 294 Pages (November 2017)

Book Overview:

Learn the tricks and tips that will help you design Text Analytics solutions

This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques.

By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python.

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Advanced Predictive Techniques with Scikit-Learn and TensorFlow (Video)

Alvaro Fuentes

ISBN 13: 9781788295321 Packt Course Length: 3 hours 44 minutes (November 2017)

Video Overview:

Improve the performance predictive models, build more complex models and use techniques to improve quality of your predictive models.

Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems.

When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should you use, how many features are enough, should you create new features, how to combine features to give the same underlying information, which hyper-parameters should you use? You explore topics that will help you answer such questions.

Artificial Neural Networks are models loosely based on how neural networks work in a living being. These models have a long history in the Artificial Intelligence community with ups and downs in popularity. Nowadays, because of the increase in computational power, improved methods, and software enhancements, they are popular again and are the basis for advanced approaches such as Deep Learning. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library.

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Cryptography with Python (Video)

Sam Bowne

ISBN 13: 9781788397179 Packt Course Length: 1 hours 30 minutes (November 2017)

Video Overview:

Encrypt, evaluate, compare, and attack your data

Cryptography is essential to protect sensitive information, but it is often performed inadequately or incorrectly. Learn how to encrypt data, evaluate and compare encryption methods, and how to attack them. This video course starts by showing you how to encrypt and evaluate your data. You are also walked through various data encryption methods—such as obfuscation, hashing, and strong encryption—and how you can attack them. You will then learn how to make hashes and crack them, and understand why they are so different. You will also learn how to use three NIST-recommended systems: AES, SHA, and RSA. Towards the end of the course, you will master common errors in encryption and how to exploit them.

The goal of this course is to show viewers how to encrypt data, evaluate and compare encryption methods, and attack them.

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Scaling Python

Julien Danjou

The Hacker's Guide to Scaling Python cover

ISBN: 978-1-387-37932-3 – 300 pages – (December 2017)

Book Overview:

Python is a wonderful programming language that allows to write applications quickly. But how do you make those applications scale for thousands of users and requests? It takes years of practice, research, trial and errors to build experience and knowledge along the way. Simple questions such as "How do I make my code faster?" or "How do I make sure there is no bottleneck?" cost hours to find good answers. Without enough background on the topic, you'll never be sure that any answer you'll come up with will be correct. The Hacker's Guide to Scaling Python will help you solve that by providing guidelines, tips and best practice. Adding a few interview of experts on the subject, you will learn how you can distribute your Python application so it is able to process thousands of requests.

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Matplotlib for Python Developers (Video)

Benjamin Keller

ISBN 13: 9781787281998 Packt Publishing Course Length: 2 hours 56 minutes (October 2017)

Video Overview:

Understand the basic fundamentals of plotting and data visualization using Matplotlib

In this course, you hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. You understand data dimensionality and set up an environment by beginning with basic plots. You enter into the exciting world of data visualization and plotting. You'll work with line and scatter plots and construct bar plots and histograms. You'll also explore images, contours, and histograms in depth. Plot scaffolding is a very interesting topic wherein you'll be taken through axes and figures to help you design excellent plots. You'll learn how to control axes and ticks, and change fonts and colors. You'll work on backends and transformations. Then lastly you'll explore the most important companions for Matplotlib, Pandas and Jupyter, used widely for data manipulation, analysis, and visualization.

By the end of this course you'll be able to construct effective and beautiful data plots using the Matplotlib library for the Python programming language.

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Python GUI Programming Recipes using PyQt5 (Video)

Burkhard Meier

ISBN 13: 9781788471268 Packt Publishing Course Length: 4 hours 09 minutes (October 2017)

Video Overview:

Learn to design a UI with help of PyQT5

In this video, you will successfully install PyQt5 and the toolset that contains the QT Designer tool. The QT Designer enables you to develop our GUI in a visual manner, using drag and drop to add and position widgets, and we will use it extensively. We will then learn how to convert QT Designer-generated code into pure Python code.

After having successfully installed PyQt5, the QT Designer, and all other required QT tools, you will start out simple, building a Python GUI using only a few lines of PyQT5 code. Then, you will build a more complex GUI using QT Designer. Along the way, you will explore many QT widgets and learn how to efficiently lay out our GUI design. You will enhance the look-and-feel of the GUI using CSS styling. You will also connect our GUI to a SQL database, which we will create. You decouple the business logic code from the UI code, using best practices. At the end of this video tutorial, viewers will be able to develop complex GUIs using PyQt5.

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Python Design Patterns (Video)

Tong Qiu

ISBN 13: 9781786460677 Packt Publishing Course Length: 2 hours 26 minutes (September 2017)

Video Overview:

Design patterns to improve the speed, code reuse, and performance of your Python applications.

This course focuses on showing you the practical aspects of smarter coding in Python.

Get into the world of design patterns and brush up on your OOP skills. Explore the most widely used patterns and create objects in a manner best suited to the situation. Some patterns will help you identify simple ways to realize relationships between entities. Next, learn how to encapsulate behaviour in an object and delegate requests to it, before delving into some advanced patterns. With this course, you will be well equipped to craft faster, cleaner, and smarter applications.

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Mastering Python Data Analysis with Pandas (Video)

Prabhat Ranjan

ISBN 13: 9781787280083 Packt Publishing Course Length: 1 hour 17 minutes (September 2017)

Video Overview:

Learn how to use Pandas, the Python library for data and statistical analysis.

This course is your guide to implementing the more advanced offerings of the popular Pandas library and explains how it can solve real-world problems. After a brief overview of the basics—such as data structures and various data manipulation tasks such as grouping, merging, and reshaping data—this video also teaches you how to manipulate, analyse, and visualize your time-series financial data.

You will learn how to apply Pandas to important but simple financial tasks such as modelling portfolios, calculating optimal portfolios based upon risk, and more. This video not only teaches you why Pandas is a great tool for solving real-world problems in quantitative finance, it also takes you meticulously through every step of the way, with practical, real-world examples, especially from the financial domain where Pandas is a popular choice. By the end of this video, you will be an expert in using the Pandas library for any data analysis problem, especially related to finance.

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Architectural Patterns and Best Practices with Python (Video)

Anand Balachandran Pillai

ISBN 13: 9781788838276 Packt Publishing Course Length: 1 hour 37 minutes (September 2017)

Video Overview:

Implement various Architectural patterns and learn to deploy and debug your Python applications.

This video starts off by explaining how Python fits into application architecture. As you move along, you will understand the architecturally significant demands and how to determine them. Later, you’ll get a complete understanding of the different architectural patterns such as event driven programming, microservice architecture and pipe and filter architecture. Python, as an open-source language, has matured in the level of automation and support it provides for deploying packages to production systems. Get to understand deployment of Python applications, and the tools and processes that the architect can add to his repertoire in order to ease the deploying and maintenance of his production systems' running applications, written using Python.

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Python 3.x for Computer Vision (Video)

Saurabh Kapur

ISBN 13: 9781788838207 Packt Publishing Course Length: 1 hour 29 minutes (September 2017)

Video Overview:

Unleash the power of computer vision with Python to carry out image processing and computer vision techniques.

This video course is a practical guide for developers who want to get started with building computer vision applications using Python 3. Throughout this video course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms.

The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we look at in the course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet.

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Data Acquisition and Manipulation with Python (Video)

Curtis Miller

ISBN 13: 9781788291415 Packt Publishing Course Length: 2 hours 39 minutes (September 2017)

Video Overview:

Acquire and analyse data in different formats with the help of Python data analysis tools.

In this course, you’ll start by learning how to acquire data from the web in its already “clean” format, such as in a .csv file, or a database. You’ll then learn to transform this data so it’s in its most useful format for analysis. After that, you’ll dive into data aggregation and grouping, where you’ll learn to group similar data for easier analysis purposes. From there, you’ll be shown different methods of web scraping using Python. Finally, you’ll learn to extract large amounts of data using BeautifulSoup, as well as work with Selenium and Scrapy.

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Python For Android Hacking Crash Course: Trojan Perspective (Video)

Hussam Khrais

ISBN 13: 9781788626255 Packt Publishing Course Length: 3 hours 16 minutes (September 2017)

Video Overview:

Acquire and analyse data in different formats with the help of Python data analysis tools.

This friendly course takes you through Python For Android Hacking Crash Course. It is packed with step-by-step instructions and working examples. This comprehensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

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Python For Offensive PenTest: A Complete Practical Course (Video)

Hussam Khrais

ISBN 13: 9781788628068 Packt Publishing Course Length: 5 hours 57 minutes (September 2017)

Video Overview:

Python for Hacking , Learn how to use python for ethical hacking and penetration testing.

Firstly, download the script, read the inline comments, run the script in your home lab, then finally see the explanatory video. Learn how to code your own reverse shell [TCP+HTTP]. Make anonymous shell by interacting with [Twitter, Google Form, Sourceforge]. Replicate Metasploit features and make an advanced shell and much more.

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Python Machine Learning - Second Edition

Sebastian Raschka, Vahid Mirjalili

ISBN 13: 9781787125933 Packt Publishing 622 pages (September 2017)

Python Machine Learning - Second Edition cover

Book Overview:

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries

Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.

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Practical Python Data Science Techniques (Video)

Marco Bonzanini

ISBN 13: 9781788294294 Packt Publishing Course Length: 2 hours 32 minutes (August 2017)

Video Overview:

Learn practical solutions to Data Science problems with Python

A comprehensive course packed with step-by-step instructions, working examples, and helpful advice on Data Science Techniques in Python. This comprehensive course is divided into clear bite size chunks so you can learn at your own pace and focus on the areas that interest you the most.

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Making Predictions with Data and Python (Video)

Alvaro Fuentes

ISBN 13: 9781788297448 Packt Publishing Course Length: 4 hours 10 minutes (August 2017)

Video Overview:

Build Awesome Predictive Models with Python

This course introduces the main concepts, techniques, and best practices for doing Predictive Analytics with Python. Using an example-based approach, it covers all the stages in the process of building predictive models with Python. By the end of the course you will be able to build Predictive Analytics models using real-world data.

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Python Network Programming Cookbook - Second Edition

Pradeeban Kathiravelu, Dr. M. O. Faruque Sarker

ISBN 13: 9781786463999 Packt Publishing 450 pages (August 2017)

Book Overview:

Discover practical solutions for a wide range of real-world network programming tasks

Python Network Programming Cookbook - Second Edition highlights the major aspects of network programming in Python, starting from writing simple networking clients to developing and deploying complex Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) systems.

In this edition, you will also be introduced to network modelling to build your own cloud network. You will learn about the concepts and fundamentals of SDN and then extend your network with Mininet. Next, you’ll find recipes on Authentication, Authorization, and Accounting (AAA) and open and proprietary SDN approaches and frameworks.

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Functional Programming in Python (Video)

Sebastiaan Mathôt

ISBN 13: 9781788292450 Packt Publishing Course Length: 2 hours and 34 minutes (July 2017)

Video Overview:

In this video course, you will learn what functional programming is, and how it differs from other programming styles, such as procedural and object-oriented programming. You will also learn why and when functional programming is useful, and why and when it makes programs unnecessarily complex. Explore lambda expressions, which are short one-line functions, and are the purest form of functional programming that Python offers. Next, learn about higher-order functions: functions that accept other functions as argument, or return other functions as return values. In Python, higher-order functions are elegantly supported through decorators. You will also encounter important concepts from functional programming, such as monads, currying, statelessness, side-effects, memoization, and referential transparency; these concepts may initially seem odd to Python programmers, but you will see how they are elegantly supported by the language. In fact, many Python programmers already make use of concepts from functional programming without being aware of doing so.

All the videos in this course contain hands-on examples of the introduced concepts.

What you will learn:

  • Recognize the value of Functional Programming
  • Understand the advantages and disadvantages of Functional Programming
  • Higher-order functions and Lambda expressions (nameless functions)
  • Error handling in Functional Programming
  • Understand common functional design patterns, and how these apply to Python

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Mastering Machine Learning with scikit-learn - Second Edition

Gavin Hackeling

ISBN 13: 9781788299879 Packt Publishing 254 pages (July 2017)

Book Overview:

This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.

By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

What you will learn:

  • Review fundamental concepts such as bias and variance
  • Extract features from categorical variables, text, and images
  • Predict the values of continuous variables using linear regression and K Nearest Neighbors
  • Classify documents and images using logistic regression and support vector machines
  • Create ensembles of estimators using bagging and boosting techniques
  • Discover hidden structures in data using K-Means clustering
  • Evaluate the performance of machine learning systems in common tasks

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Python Natural Language Processing

Jalaj Thanaki

ISBN 13: 9781787121423 Packt Publishing 486 pages (July 2017)

Book Overview:

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them.

What you will learn:

  • Focus on Python programming paradigms, which are used to develop NLP applications
  • Understand corpus analysis and different types of data attribute
  • Learn NLP using Python libraries such as NLTK, Polyglot, SpaCy, Standford CoreNLP and so on

  • Learn about Features Extraction and Feature selection as part of Features Engineering
  • Explore the advantages of vectorization in Deep Learning
  • Get a better understanding of the architecture of a rule-based system
  • Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems
  • Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems

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Cloud Native Python

Manish Sethi

ISBN 13: 9781787129313 Packt Publishing 374 pages (July 2017)

Book Overview:

This book will be the one stop for you to learn all about building cloud-native architectures in Python. It will begin by introducing you to cloud-native architecture and will help break it down for you. Then you’ll learn how to build microservices in Python using REST APIs in an event driven approach and you will build the web layer. Next, you’ll learn about Interacting data services and building Web views with React, after which we will take a detailed look at application security and performance. Then, you’ll also learn how to Dockerize your services. And finally, you’ll learn how to deploy the application on the AWS and Azure platforms.

This book will teach you how to craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: we're going to build everything using Python 3 and its amazing tooling ecosystem. The book will take you on a journey, the destination of which, is the creation of a complete Python application based on microservices over the cloud platform.

What you will learn:

  • Get to know “the way of the cloud”, including why developing good cloud software is fundamentally about mindset and discipline
  • Know what microservices are and how to design them
  • Create reactive applications in the cloud with third-party messaging providers
  • Build massive-scale, user-friendly GUIs with React and Flux
  • Secure cloud-based web applications: the do’s, don’ts, and options
  • Plan cloud apps that support continuous delivery and deployment

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Python Social Media Analytics

Siddhartha Chatterjee, Michal Krystyanczuk

ISBN 13: 9781787121485 Packt Publishing 312 pages (July 2017)

Book Overview:

Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup.

Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes.

What you will learn:

  • Understand the basics of social media mining
  • Use PyMongo to clean, store, and access data in MongoDB

  • Understand user reactions and emotion detection on Facebook
  • Perform Twitter sentiment analysis and entity recognition using Python
  • Analyze video and campaign performance on YouTube

  • Mine popular trends on GitHub and predict the next big technology

  • Extract conversational topics on public internet forums
  • Analyze user interests on Pinterest
  • Perform large-scale social media analytics on the cloud

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Python Microservices Development

Tarek Ziadé

ISBN 13: 9781785881114 Packt Publishing 340 pages (July 2017)

Book Overview:

An efficient way to build applications to do this is through microservices architecture. But, in practice, it's hard to get this right due to the complexity of all the pieces interacting with each other.

This book will teach you how to overcome these issues and craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: you’ll build everything using Python 3 and its amazing tooling ecosystem. You will understand the principles of TDD and apply them.

This book will take you on a journey, ending with the creation of a complete Python application based on microservices. By the end of the book, you will be well versed with the fundamentals of building, designing, testing, and deploying your Python microservices.

What you will learn:

  • Explore what microservices are and how to design them
  • Use Python 3, Flask, Tox, and other tools to build your services using best practices
  • Learn how to use a TDD approach
  • Discover how to document your microservices
  • Configure and package your code in the best way
  • Interact with other services
  • Secure, monitor, and scale your services
  • Deploy your services in Docker containers, CoreOS, and Amazon Web Services

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Mastering Python Networking

Eric Chou

ISBN 13: 9781784397005 Packt Publishing 446 pages (June 2017)

Book Overview:

This book begins with a review of the TCP/ IP protocol suite and a refresher of the core elements of the Python language. Next, you will start using Python and supported libraries to automate network tasks from the current major network vendors. You will look at automating traditional network devices based on the command-line interface, as well as newer devices with API support, with hands-on labs. You will then learn the concepts and practical use cases of the Ansible framework in order to achieve your network goals.

You will then move on to using Python for DevOps, starting with using open source tools to test, secure, and analyze your network. Then, you will focus on network monitoring and visualization. You will learn how to retrieve network information using a polling mechanism, flow-based monitoring, and visualizing the data programmatically. Next, you will learn how to use the Python framework to build your own customized network web services.

In the last module, you will use Python for SDN, where you will use a Python-based controller with OpenFlow in a hands-on lab to learn its concepts and applications. Compare and contrast OpenFlow, OpenStack, OpenDaylight, and NFV. Finally, you will use everything you’ve learned in the book to construct a migration plan to go from a legacy to a scalable SDN-based network.

What you will learn:

  • Review all the fundamentals of Python and the TCP/IP suite
  • Use Python to execute commands when the device does not support the API or programmatic interaction with the device
  • Implement automation techniques by integrating Python with Cisco, Juniper, and Arista eAPI
  • Integrate Ansible using Python to control Cisco, Juniper, and Arista networks
  • Achieve network security with Python
  • Build Flask-based web-service APIs with Python
  • Construct a Python-based migration plan from a legacy to scalable SDN-based network

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Practical Data Science Cookbook - Second Edition

Prabhanjan Tattar, Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta

ISBN 13: 9781787129627 Packt Publishing 434 pages (June 2017)

Book Overview:

Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.

What you will learn:

  • Learn and understand the installation procedure and environment required for R and Python on various platforms
  • Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python
  • Build a predictive model and an exploratory model
  • Analyze the results of your model and create reports on the acquired data
  • Build various tree-based methods and Build random forest

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Unpacking NumPy and Pandas (Video)

Curtis Miller

ISBN 13: 9781787121195 Packt Publishing Course Length: 2 hours and 18 minutes (June 2017)

Video Overview:

In this video course, you will explore two of the most important Python packages used by Data Analysts. You will start off by learning how to set up the right environment for data analysis with Python. Here, you’ll learn to install the right Python distribution, as well as work with the Jupyter notebook, and set up a database. After that you will dive into Python’s NumPy package, Python’s powerful extension with advanced mathematical functions. You will learn to create NumPy arrays, as well as employ different array methods and functions. Then, you will explore Python’s Pandas extension, where you will learn to subset your data, as well as dive into data mapping using Pandas. You’ll also learn to manage your data sets by sorting and ranking them. Finally, you will learn to index and group your data for sophisticated data analysis and manipulation.

What you will learn:

  • How to read, sort, and map various data into NumPy and Pandas

  • How to create and slice data arrays using NumPy

  • How to subset your data frames using Pandas
  • How to handle missing data in a Pandas DataFrame

  • How to interface your Python data analysis with R language packages

Publisher's page


Python for Finance - Second Edition

Yuxing Yan

ISBN 13: 9781787125698 Packt Publishing 586 pages (June 2017)

Book Overview:

This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance.

The book starts by explaining topics exclusively related to Python. Then deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures.

What you will learn:

  • Become acquainted with Python in the first two chapters
  • Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models
  • Learn how to price a call, put, and several exotic options
  • Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options
  • Understand the concept of volatility and how to test the hypothesis that volatility changes over the years
  • Understand the ARCH and GARCH processes and how to write related Python programs

Publisher's page


Python Web Scraping - Second Edition

Katharine Jarmul, Richard Lawson

ISBN 13: 9781786462589 Packt Publishing 220 pages (May 2017)

Book Overview:

This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you’ll see how to extract data from static web pages. You’ll learn to use caching with databases and files to save time and manage the load on servers. After covering the basics, you’ll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers.

You’ll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You’ll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You’ll find out how to automate these actions with Python packages such as mechanize. You’ll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites.

By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics.

What you will learn:

  • Extract data from web pages with simple Python programming
  • Build a concurrent crawler to process web pages in parallel
  • Follow links to crawl a website
  • Extract features from the HTML
  • Cache downloaded HTML for reuse
  • Compare concurrent models to determine the fastest crawler
  • Find out how to parse JavaScript-dependent websites

  • Interact with forms and sessions

Publisher's page


Python High Performance - Second Edition

Gabriele Lanaro

ISBN 13: 9781787282896 Packt Publishing 270 pages (May 2017)

Book Overview:

Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications.

The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark.

By the end of the book, readers will have learned to achieve performance and scale from their Python applications.

What you will learn:

  • Write efficient numerical code with the NumPy and Pandas libraries

  • Use Cython and Numba to achieve native performance
  • Find bottlenecks in your Python code using profilers
  • Write asynchronous code using Asyncio and RxPy

  • Use Tensorflow and Theano for automatic parallelism in Python
  • Set up and run distributed algorithms on a cluster using Dask and PySpark

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Become a Python Data Analyst (Video)

Alvaro Fuentes

ISBN 13: 9781787284302 Packt Publishing Course Length: 4 hours 30 minutes (May 2017)

Video Overview:

This course introduces Python’s most important tools and libraries for doing Data Science; they are known in the community as “Python’s Data Science Stack”.

This is a practical course where the viewer will learn through real-world examples how to use the most popular tools for doing Data Science and Analytics with Python.

What you will learn:

  • Learn about the most important libraries for doing Data Science with Python and how they can be easily installed with the Anaconda distribution
  • Understand the basics of Numpy which is the foundation of all the other analytical tools in Python
  • Produce informative, useful and beautiful visualizations for analyzing data
  • Analyze, answer questions and derive conclusions from real world data sets using the Pandas library
  • Perform common statistical calculations and use the results to reach conclusions about the data
  • Learn how to build predictive models and understand the principles of Predictive Analytics

Publisher's page


Data Analysis with Python (Video)

Marco Bonzanini

ISBN 13: 9781788290548 Packt Publishing Course Length: 2 hours 26 minutes (April 2017)

Video Overview:

This course introduces the audience to the field of Data Science using Python tools to manage and analyze data. You can learn some of the fundamental tools of the trade and apply them to real data problems. And along the way it discusses the use of Python stack for data analysis and scientific computing, and expands on concepts of data acquisition, data cleaning, data analysis and machine learning.

A fun and friendly course packed with step by step instructions, it shows you how to get up and running with Python virtual environments using Conda. This course is divided into clear chunks so you can learn at your own pace.

What you will learn:

  • Installing the core Python tools for data analysis
  • Dealing with different data types in Python
  • Using NumPy for fast array computation

  • Using Pandas for data analysis
  • Framing a Data Science problem and using Python tools to solve it

Publisher's page


Learning Python Data Analysis (Video)

Benjamin Hoff

ISBN 13: 9781785880711 Packt Publishing Course Length: 5 hours 55 minutes (March 2017)

Video Overview:

This video aims to teach Python developers how to perform data analysis with the language by taking advantage of the core data science libraries in the Python ecosystem. The learning objective for viewers is to understand how to locate, manipulate, and analyse data with Python, with the ability to analyse large and small sets of data using libraries such as Numpy, pandas, IPython and SciPy.

This is a two part series. The first series is focused on getting and manipulation sizeable amounts of data using modern techniques. The second series is focused on advanced analysis of the data to include modern machine learning techniques.

What you will learn:

  • Advanced and recommend software engineering development practices
  • How to scrape the twitter stream to collect real time data
  • Smart storing of data using advanced abstractions and Object-Oriented programming
  • Easy and practical data manipulation techniques for dealing with large volumes of data
  • Natural Language Processing tools, special designed for working with sentences and other forms of textual data
  • Predictive methods that can forecast and predict future trends based on current data
  • Data analytics techniques to tease out unseen data relationships
  • Dashboard application development to help share and monitor your progress/analysis

Publisher's page


Getting Started with Python Web Scraping (Video)

Charles Clayton

ISBN 13: 9781787283244 Packt Publishing Course Length: 1 hour 36 minutes (March 2017)

Video Overview:

This video course is a rich collection of recipes that will come in handy when you are scraping a website using Python, addressing your usual and unusual problems while scraping websites by diving deep into the capabilities of Python’sweb scraping tools such as Selenium, BeautifulSoup, and urllib2. The video will start with showing how to use selenium module for scraping by setting up a web driver, debugging with the Console and downloading files and streamlining with a Headless Browser (PhantomJS). The video will then move on to demonstrate how to do parsing with Beautifulsoup which would include introduction to the BeautifulSoupObjects, Nested Selectors and Regular Expressions Basics and how to do UTF-8 Encoding. The video will finally end by showing how to do fetching with urlib2 by using the developer tools Network tab, how to bypass the browser and retrieve files.

By The end of this video, you will be successfully able to understand the in-depth capabilities of python web scraping tools.

What you will learn:

  • Use the Selenium module and scrape with Selenium
  • Find out how to set up a web driver
  • Perform debugging with the console and download files
  • Learn to work with Nested selectors and regular expression basics
  • Discover how to perform parsing with BeautifulSoup

  • Understand authentication with Wireshark.
  • Master the use of URL Query Strings and HTTP Requests (GET and POST)
  • Implement streamlining with headless browser

Publisher's page


Python: Step into the World of Machine Learning

Prateek Joshi, Alexander T. Combs, Dan Van Boxel, Giancarlo Zaccone, Sebastian Raschka, Luis Pedro Coelho, Willi Richert

Packt Publishing Duration: 7 hours (January 2017)

Course Overview:

This course takes a hands-on approach and demonstrates how you can perform various Machine Learning tasks on real-world data. The course starts by talking about various realms in machine learning followed by practical examples. It then moves on to discuss the more complex algorithms, such as Support Vector Machines, Extremely Random Forests, Hidden Markov Models, Sentiment Analysis, and Conditional Random Fields. You will learn how to make informed decisions about the types of algorithm that you need to use and how to implement these algorithms to get the best possible results.

After you are comfortable with machine learning, this course teaches you how to build real-world machine learning applications step by step. Further, we’ll explore deep learning with TensorFlow, which is currently the hottest topic in data science. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change the way you look at data. You will also learn how to train your machine to craft new features to make sense of deeper layers of data.

What you will learn:

  • Explore and use Python’s impressive machine learning ecosystem
  • Understand the different types of machine learning
  • Learn predictive modeling and apply it to real-world problems
  • Work with image data and build systems for image recognition and biometric face recognition
  • Build your own applications using machine learning
  • Build simple TensorFlow graphs for everyday computations

Publisher's page


Flask: Building Python Web Services

Gareth Dwyer, Shalabh Aggarwal, Jack Stouffer

ISBN 13: 9781787288225 Packt Publishing 770 pages (March 2017)

Course Overview:

This course will take you through the intricacies of the Flask Microframework, covering all it's components and elements and how to integrate it with useful third party libraries. Dive deep into what Flask has to offer and then you will create multiple Python apps from scratch on your own.

  • Build three web applications from the ground up using the powerful Python micro framework, Flask.
  • Extend your applications to build advanced functionality, such as a user account control system using Flask-Login
  • Learn about web application security and defend against common attacks, such as SQL injection and XSS
  • Integrate with technologies like Redis, Sentry, MongoDB and so on
  • Build applications with integrations to most of the login mechanisms available
  • Don't just stop at development. Learn about deployment and post-deployment
  • Use SQLAlchemy to programmatically query a database
  • Develop a custom Flask extension

Who this course is written for:

This learning path is ideal developers who know the basics of Python and want to learn how to use the Flask framework to build powerful web solutions in Python.

Publisher's page


Automate it! - Recipes to upskill your business

Chetan Giridhar

ISBN 13: 9781786460516 Packt Publishing 392 pages (January 2017)

Book Overview:

This book gives you a great selection of recipes to automate your business processes with Python, and provides a platform for you to understand how Python is useful to make time consuming and repetitive business tasks more efficient. You will begin by learning about the Python modules to work with Web, Worksheets, Presentations and PDFs. You’ll leverage Python recipes to automate processes in HR, Finance and making them efficient and reliable. For instance, company payroll — an integral process in HR will be automated with Python recipes.

  • Implement file deduplication and how to parse HTML content in Python.
  • Study an example application that will enable you to work with spreadsheets
  • Get acquainted with the Python modules used to work with e-mails
  • Manipulate images using Pillow and schedule tasks with respect to time zones
  • Use XML or JSON as a message format across distributed systems
  • Develop a Python application with logging and see an example of debugging
  • Integrate Python with Mongo and Redis databases
  • Generate reports using Pandas

Who this book is written for:

This book is for programmers who know Python but need not be experts. They will however know a lot of the basics of the syntax and some basic experience with the command line. Ultimately, they’re people who see themselves as busy and want to find cool hacks to automate all the mundane and time-consuming tasks they find themselves doing on a daily basis.

Publisher's page


Scientific Computing with Python 3

Claus Führer, Jan Erik Solem, Olivier Verdier

ISBN 13: 9781786463517 Packt Publishing 332 pages (December 2016)

Book Overview:

This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.

  • The principal syntactical elements of Python
  • The most important and basic types in Python
  • The essential building blocks of computational mathematics, linear algebra, and related Python objects
  • Plot in Python using matplotlib to create high quality figures and graphics to draw and visualize your results
  • Define and use functions and learn to treat them as objects
  • How and when to correctly apply object-oriented programming for scientific computing in Python
  • Handle exceptions, which are an important part of writing reliable and usable code
  • Two aspects of testing for scientific programming: Manual and Automatic

Who this book is written for:

This book is for anyone who wants to perform numerical and mathematical computations in Python. It is especially useful for developers, students, and anyone who wants to use Python for computation. Readers are expected to possess basic a knowledge of scientific computing and mathematics, but no prior experience with Python is needed.

Publisher's page


Mastering Python - Second Edition (Video)

Daniel Arbuckle

ISBN 13: 9781786463746 Packt Publishing Course Length 5 hours 21 minutes (October 2016)

Video Overview:

You will cover the basics of operating in a Python development environment as well as the advanced topics. We present you with real-world solutions to Python 3.5 and advanced-level concepts such as reactive programming and microservices, introduce ctypes and Cython tools. Throughout the journey, we'll highlight the major aspects of managing your Python development environment, show you how to handle parallel computation, and help you to master asynchronous I/O with new Python 3.5 to improve the performance of your system. Finally, you'll learn the secrets of metaprogramming and unit testing in Python—arming you with the perfect skillset to be a Python expert. This course will get you up to speed in everything from basic programming practices to high-end tools and techniques, things that will help you set apart as a successful Python programmer.

  • Get to grips with the basics of operating in a Python development environment
  • Build Python packages to efficiently create reusable code
  • Become proficient at creating tools and utility programs in Python
  • Use the Git version control system to protect your development environment from unwanted changes
  • Harness the power of Python to automate other software
  • Distribute computation tasks across multiple processors
  • Handle high I/O loads with asynchronous I/O to get a smoother performance
  • Take advantage of Python's metaprogramming and programmable syntax features
  • Get acquainted to the concepts behind reactive programming and RxPy

Who this video course is for:

If you are a programmer who is familiar with the basics of Python and you want to broaden your knowledge base to develop projects better and faster, this course is for you. Even if you are not familiar with Python, our course starts with the basics and takes you on a journey to become an expert in the technology.

Publisher's page


Python Data Science Essentials - Second Edition

Alberto Boschetti, Luca Massaron

ISBN 13: 9781786462138 Packt Publishing 378 pages (October 2016)

Book Overview:

With this book you'll get modern insight of the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.

  • Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux
  • Get data ready for your data science project
  • Manipulate, fix, and explore data in order to solve data science problems
  • Set up an experimental pipeline to test your data science hypotheses
  • Choose the most effective and scalable learning algorithm for your data science tasks
  • Optimize your machine learning models to get the best performance
  • Explore and cluster graphs, taking advantage of interconnections and links in your data

Who this book is written for:

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

Publisher's page


Building RESTful Python Web Services

Gastón C. Hillar

ISBN 13: 978178646225 Packt Publishing 418 (October 2016)

Book Overview:

Learn how to develop RESTful APIs using the popular Python frameworks and all the necessary stacks with Python, Django, Flask, and Tornado, combined with related libraries and tools. We will dive deep into each of these frameworks to build various web services, and will provide use cases and best practices on when to use a particular framework to get the best results.

  • Develop complex RESTful APIs from scratch with Python combined with and without data sources
  • Choose the most appropriate (micro) framework based on the specific requirements of a RESTful API / web service
  • Debug, test, and profile RESTful APIs with each of the frameworks
  • Develop a complex RESTful API that interacts with a PostgreSQL database
  • Add authentication and permissions to a RESTful API built in each of the frameworks
  • Map URL patterns to request handlers and check how the API works
  • Profile an existing API and refactor it to take advantage of asynchronous code

Who this book is written for:

This book is for web developers who have working knowledge of Python and would like to build amazing web services by taking advantage of the various frameworks of Python. You should have some knowledge of RESTful APIs.

Publisher's page


Python: Penetration Testing for Developers

Christopher Duffy et al.

ISBN 13: 9781787128187 Packt Publishing 650 pages (October 2016)

Course Overview:

This course shows you how to do just that, demonstrating how effective Python can be for powerful pentesting that keeps your software safe. Comprising of three key modules, follow each one to push your Python and security skills to the next level.In the first module, we’ll show you how to get to grips with the fundamentals. This means you’ll quickly find out how to tackle some of the common challenges facing pentesters using custom Python tools designed specifically for your needs. You’ll also learn what tools to use and when, giving you complete confidence when deploying your pentester tools to combat any potential threat.In the next module you’ll begin hacking into the application layer. Covering everything from parameter tampering, DDoS, XXS and SQL injection, it will build on the knowledge and skills you learned in the first module to make you an even more fluent security expert.Finally in the third module, you’ll find more than 60 Python pentesting recipes. We think this will soon become your trusted resource for any pentesting situation.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

Learning Penetration Testing with Python by Christopher Duffy

Python Penetration Testing Essentials by MohitPython

Web Penetration Testing Cookbook by Cameron Buchanan,Terry Ip, Andrew Mabbitt, Benjamin May and Dave Mound

  • Familiarize yourself with the generation of Metasploit resource files and use the Metasploit Remote Procedure Call to automate exploit generation and execution
  • Exploit the Remote File Inclusion to gain administrative access to systems with Python and other scripting languages
  • Crack an organization's Internet perimeter and chain exploits to gain deeper access to an organization's resources
  • Explore wireless traffic with the help of various programs and perform wireless attacks with Python programs
  • Gather passive information from a website using automated scripts and perform XSS, SQL injection, and parameter tampering attacks
  • Develop complicated header-based attacks through Python

Who this course is for:

If you are a Python programmer or a security researcher who has basic knowledge of Python programming and wants to learn about penetration testing with the help of Python, this course is ideal for you. Even if you are new to the field of ethical hacking, this course can help you find the vulnerabilities in your system so that you are ready to tackle any kind of attack or intrusion.

Publisher's page


Natural Language Processing: Python and NLTK

Nitin Hardeniya et al.

ISBN 13: 9781787285101 Packt Publishing 687 pages (November 2016)

Course Overview:

The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy.The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products:

NTLK essentials by Nitin Hardeniya

Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins

Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur

  • The scope of natural language complexity and how they are processed by machines
  • Clean and wrangle text using tokenization and chunking to help you process data better
  • Tokenize text into sentences and sentences into words
  • Classify text and perform sentiment analysis
  • Implement string matching algorithms and normalization techniques
  • Understand and implement the concepts of information retrieval and text summarization
  • Find out how to implement various NLP tasks in Python

Who this course is for:

If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable.

Publisher's page


Python Projects [Video]

Burkhard A. Meier

ISBN 13: 9781786466990 Packt Publishing Course Length 2 hours 19 minutes (November 2016)

Video Overview:

This video will teach you how to create well designed architecture and increase performance of the current applications.You will learn how to build enterprise ready applications with Python language.It will also demonstrate how to manage large user bases and ensure that the application is scaled easily. The projects will have an overall focus ranging from web development, statistics, etc. These ready-to-use solutions will appeal to wide range of audience and will teach important programming concepts of Python along the way.By the end of the course, you will be able to build 4 real world production ready applications. And you can go forward very far from this very strong foundation.

  • Develop Python applications in a world-class IDE
  • Create fully functional GUIs written in Python effortlessly
  • Automate your tasks by using scheduling mechanisms
  • Send HTML formatted emails
  • Implement several Design Patterns in Python
  • Create a Windows Service in Python

Who this video for:

The video would appeal to web developers,statisticians, programmers, data scientist, Python consultants and anyone who is working on multiple projects with Python. Basic knowledge of Python programming is assumed.

Publisher's page


Python Data Visualization Solutions [Video]

Dimitry Foures, Giuseppe Vettigli, Igor Milovanović

ISBN 13: 9781787122802 Packt Publishing Course Length 3 hours 27 minutes (November 2016)

Video Overview:

This video starts by showing you how to set up matplotlib and other Python libraries that are required for most parts of the course, before moving on to discuss various widely used diagrams and charts such as Gantt Charts. As you will go through the course, you will get to know about various 3D diagrams and animations. As maps are irreplaceable to display geo-spatial data, this course will show you how to build them. In the last section, we’ll take you on a thorough walkthrough of incorporating matplotlib into various environments and how to create Gantt charts using Python.With practical, precise, and reproducible videos, you will get a better understanding of the data visualization concepts, how to apply them, and how you can overcome any challenge while implementing them.

  • Draw your first chart and customize it
  • Use the most popular data visualization Python libraries
  • Make 3D visualizations mainly using mplot3d
  • Create charts with images and maps
  • Understand the most appropriate charts to describe your data
  • Get to know the matplotlib’s hidden gems

Who this video is for:

If you are an analyst or a budding data scientist who wants to know how to use Python to visualize your data to get effective insights from it, then this book is for you. Some understanding of Python programming will be useful.

Publisher's page


Modern Python Cookbook

Steven F. Lott

ISBN 13: 9781786469250 Packt Publishing 824 pages (November 2016)

Book Overview:

This book comes with over 100 recipes on the latest version of Python. The recipes will benefit everyone ranging from beginner to an expert. The book is broken down into 13 chapters that build from simple language concepts to more complex applications of the language.The recipes will touch upon all the necessary Python concepts related to data structures, OOP, functional programming, as well as statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively use the advantages that it offers. You will end the book equipped with the knowledge of testing, web services, and configuration and application integration tips and tricks.

  • See the intricate details of the Python syntax and how to use it to your advantage
  • Improve your code readability through functions in Python
  • Manipulate data effectively using built-in data structures
  • Get acquainted with advanced programming techniques in Python
  • Equip yourself with functional and statistical programming features
  • Write proper tests to be sure a program works as advertised
  • Integrate application software using Python

Who this book is written for:

The book is for web developers, programmers, enterprise programmers, engineers, big data scientist, and so on. If you are a beginner, Python Cookbook will get you started. If you are experienced, it will expand your knowledge base. A basic knowledge of programming would help.

Publisher's page


Python: Master the Art of Design Patterns

By Dusty Phillips, Chetan Giridhar, Sakis Kasampalis

ISBN 13: 9781787125186 Packt Publishing 775 pages (September 2016)

Course Overview:

This learning path takes you through every traditional and advanced design pattern best applied to Python code, building your skills in writing exceptional Python. Divided into three distinct modules, you’ll go from foundational to advanced concepts by following a series of practical tutorials.Start with the bedrock of Python programming – the object-oriented paradigm.

Rethink the way you work with Python as you work through the Python data structures and object-oriented techniques essential to modern Python programming. Build your confidence as you learn Python syntax, and how to use OOP principles with Python tools such as Django and Kivy.

In the second module, run through the most common and most useful design patterns from a Python perspective. Progress through Singleton patterns, Factory patterns, Façade patterns and more all with detailed hands-on guidance. Enhance your professional abilities in in software architecture, design, and development.In the final module, run through the more complex and less common design patterns, discovering how to apply them to Python coding with the help of real-world examples.Get to grips with the best practices of writing Python, as well as creating systems architecture and troubleshooting issues.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

Python 3 Object-Oriented Programming - Second Edition by Dusty PhillipsLearning

Python Design Patterns - Second Edition by Chetan GiridharMastering

Python Design Patterns by Sakis Kasampalis

  • Discover what design patterns are and how to apply them to writing Python
  • Implement objects in Python by creating classes and defining methods
  • Separate related objects into a taxonomy of classes and describe the properties and behaviors of those objects via the class interface
  • Understand when to use object-oriented features, and more importantly when not to use them
  • Get to know proven solutions to common design issues
  • Explore the design principles that form the basis of software design, such as loose coupling, the Hollywood principle, and the Open Close principle, among others
  • Use Structural Design Patterns and find out how objects and classes interact to build larger applications
  • Improve the productivity and code base of your application using Python design patterns
  • Secure an interface using the Proxy pattern

Who this course is written for:

If you have basic Python skills and wish to learn in depth how to correctly apply appropriate design patterns, this course is tailor made for you.

Publisher's page


Text Analytics with Python - 1st Edition

By Dipanjan Sarkar

Text Analytics with Python cover

ISBN-13: 978-1484223871

ISBN-10: 978-1484223871

Release Date: December 2016

Pages: 385

Book Overview:

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem.

Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.

A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

  • Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics.
  • Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews.
  • Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern

Who this book is for:

This book is aimed at IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data.

Publisher's Page (Apress)

Publisher's Page (Springer)

Author's Page with code & bonus content


The Hacker's Guide to Python – 3rd edition

By Julien Danjou

The Hacker's Guide to Python cover

ISBN 13 978-1-329-98474-5 290 Pages (May 2016)

Book overview:

  • Best practice: methods & advice you should follow when building your applications.

  • Language internals: get introduced to some of the Python internals and get a better understanding of how to write more efficient code. Gain a greater insight into the inner workings of the language.
  • Solve problems: battle-tested solutions to tackle problems such as testing, porting, or scaling Python applications and libraries. Discover strategies that will help you maintain your software in the long term.
  • Interviews with Python software developers and experts.

Who this book is for

This book is aimed at developers who already know Python but wants to learn from more experienced Python developers.

Publisher's page


Mastering Flask (Video)

Alexander Putilin, Jack Stouffer

ISBN 13: 9781784393915 4 hours 14 minutes (November 2016)

Video Overview:

This course will take you deep into the world of using Flask and its ecosystem of extensions to create web applications. We’ll walk through creating a simple IMDB clone from scratch. We’ll start by creating the boilerplate code and use Virtualenv to create an isolated development environment. You’ll then learn to work with the database using SQLAlchemy. After that, we’ll display our data to the end user using WTForms.

  • Build a real-world application that adheres to best practices using Flask
  • Use Virtualenv to incorporate dependency isolation
  • Work with SQLAlchemy while learning database concepts
  • See how to customize Jinja templates to work with dynamic pages
  • Create secure forms using WTForms
  • Modularize your code with Blueprints
  • Work with Flask Login and Flask Principal to secure our app
  • Add a REST API to our app to allow programmers to easily build off the platform the app is building
  • Create an administrator interface using Flask Admin
  • Speed up the working of the app with Flask Debug Toolbar, Flask Cache, and Flask Assets
  • Implement asynchronous programming using Celery
  • Make the app robust by performing various tests on it
  • Deploy the app to platforms such as AWS, Heroku, and simple VPS with Nginx and Gunicorn

Who is this video course for:

If you are a Flask user who knows the basics of the library and how to create basic web pages with HTML and CSS, and you want to take your applications to the next level, then this video course is for you. Harnessing the full power of Flask will allow you to create complex web applications with ease.

Publisher's Page


Learning Python Application Development

Ninad Sathaye

ISBN 13: 9781785889196 Pages 454 (September 2016)

Book Overview:

This book will show you ways to handle such problems and write better Python applications.From the basics of simple command-line applications, develop your skills all the way to designing efficient and advanced Python apps. Guided by a light-hearted fantasy learning theme, overcome the real-world problems of complex Python development with practical solutions. Beginning with a focus on robustness, packaging, and releasing application code, you’ll move on to focus on improving application lifetime by making code extensible, reusable, and readable. Get to grips with Python refactoring, design patterns and best practices. Techniques to identify the bottlenecks and improve performance are covered in a series of chapters devoted to performance, before closing with a look at developing Python GUIs.

  • Build a robust application by handling exceptions.
  • Modularize, package, and release the source distribution.
  • Document the code and implement coding standards.
  • Create automated tests to catch bugs in the early development stage.
  • Identify and re-factor badly written code to improve application life.
  • Detect recurring problems in the code and apply design patterns.
  • Improve code efficiency by identifying performance bottlenecks and fixing them.
  • Develop simple GUI applications using Python.

Who is this book for:

Do you know the basics of Python and object oriented programming? Do you want to go an extra mile and learn techniques to make your Python application robust, extensible, and efficient? Then this book is for you.

Publisher's Page


Raspberry Pi Cookbook for Python Programmers (Free eBook)

Tim Cox

ISBN 13: 9781849696623 402 Pages (April 2014)

Book Overview:

Raspberry Pi Cookbook for Python Programmers is a practical guide for getting the most out of this little computer. It will guide you through the many uses of the Raspberry Pi and allow you to showcase the best it has to offer. Discover what the Raspberry Pi has to offer using detailed Python 3 examples that you can adapt and extend; see where your creativity takes you!

  • Set up and run Raspberry Pi for the first time
  • Develop desktop applications, and handle images and process files with ease
  • Make use of graphics and user control to develop your own exciting games
  • Create 3D worlds by using the Raspberry Pi's powerful GPU
  • Discover how to create your own electronic circuits to interact with the Raspberry Pi
  • Use the Raspberry Pi Camera to create animations and timelapses
  • Design and build your own Raspberry Pi controlled robots
  • Take control of the real world and interface with physical hardware, combining hardware and software for your own needs

Publisher's Page


Building Machine Learning Systems with Python (Free eBook)

Willi Richert, Luis Pedro Coelho

ISBN 13: 9781782161400 290 Pages (July 2013)

Book Overview:

Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.

  • Build a classification system that can be applied to text, images, or sounds
  • Use scikit-learn, a Python open-source library for machine learning
  • Explore the mahotas library for image processing and computer vision
  • Build a topic model of the whole of Wikipedia
  • Get to grips with recommendations using the basket analysis
  • Use the Jug package for data analysis
  • Employ Amazon Web Services to run analyses on the cloud
  • Recommend products to users based on past purchases

Expand your knowledge of Python data with the power of machine learning with this free and full-featured guide.

Publisher's Page


Python: Deeper Insights into Machine Learning

By Sebastian Raschka, David Julian, John Hearty

ISBN 13: 9781787128576 Packt Publishing 901 pages (August 2016)

Course overview:

The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems. At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.

  • Learn to write clean and elegant Python code that will optimize the strength of your algorithms
  • Uncover hidden patterns and structures in data with clustering
  • Improve accuracy and consistency of results using powerful feature engineering techniques
  • Gain practical and theoretical understanding of cutting-edge deep learning algorithms
  • Solve unique tasks by building models
  • Get grips on the machine learning design process

Who this course is written for:

This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.

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Mastering Predictive Analytics with Python

By Joseph Babcock

ISBN 13: 9781785882715 Packt Publishing 334 pages (August 2016)

Book overview:

In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life

  • Gain an insight into components and design decisions for an analytical application
  • Master the use Python notebooks for exploratory data analysis and rapid prototyping
  • Get to grips with applying regression, classification, clustering, and deep learning algorithms
  • Discover the advanced methods to analyze structured and unstructured data
  • Find out how to deploy a machine learning model in a production environment
  • Visualize the performance of models and the insights they produce
  • Scale your solutions as your data grows using Python
  • Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis

Who this course is written for:

This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You’re expected to have basic development experience with Python.

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Python: Journey from Novice to Expert

By Fabrizio Romano, Dusty Phillips, Rick van Hattem

ISBN 13: 9781787120761 Packt Publishing 1311 pages (August 2016)

Course overview:

In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life

  • Gain an insight into components and design decisions for an analytical application
  • Master the use Python notebooks for exploratory data analysis and rapid prototyping
  • Get to grips with applying regression, classification, clustering, and deep learning algorithms
  • Discover the advanced methods to analyze structured and unstructured data
  • Find out how to deploy a machine learning model in a production environment
  • Visualize the performance of models and the insights they produce
  • Scale your solutions as your data grows using Python
  • Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis

Who this course is written for:

This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You’re expected to have basic development experience with Python.

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Large Scale Machine Learning with Python

By Bastiaan Sjardin, Luca Massaron, Alberto Boschetti

ISBN 13: 9781785887215 Packt Publishing 420 pages (August 2016)

Book Overview:

This book takes you all the way to creating a fully fledged application by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned. * Get Python up and running on Windows, Mac, and Linux in no time.

  • Grasp the fundamental concepts of coding, along with the basics of data structures and control flow.
  • Write elegant, reusable, and efficient code in any situation
  • Understand when to use the functional or the object oriented programming approach
  • Create bulletproof, reliable software by writing tests to support your code
  • Explore examples of GUIs, scripting, data science and web applications
  • Learn to be independent, capable of fetching any resource you need, as well as dig deeper

If you haven't already started setting up with Python, take advantage of this deal to ensure your Python knowledge is the best it can be.

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Mastering Data Mining with Python – Find patterns hidden in your data

By Megan SquireISBN 13: 9781785889950 Packt Publishing 268 pages (August 2016)

Book overview:

In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.

  • Explore techniques for finding frequent itemsets and association rules in large data sets
  • Learn identification methods for entity matches across many different types of data
  • Identify the basics of network mining and how to apply it to real-world data sets
  • Discover methods for detecting the sentiment of text and for locating named entities in text
  • Observe multiple techniques for automatically extracting summaries and generating topic models for text
  • See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set

Who this book is written for:

This book is for data scientists who are already familiar with some basic data mining techniques such as SQL and machine learning, and who are comfortable with Python. If you are ready to learn some more advanced techniques in data mining in order to become a data mining expert, this is the book for you!

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Data Mining with Python: Implementing Classification and Regression [Video]

By Saimadhu Polamuri

ISBN 13: 9781785885716 Packt Publishing Course Length 2 hours and 3 minutes (July 2016)

Book overview:

In this course, you will discover the key concepts of data mining and learn how to apply different data mining techniques to find the valuable insights hidden in real-world data. You will also tackle some notorious data mining problems to get a concrete understanding of these techniques. We begin by introducing you to the important data mining concepts and the Python libraries used for data mining. You will understand the process of cleaning data and the steps involved in filtering out noise and ensuring that the data available can be used for accurate analysis. You will also build your first intelligent application that makes predictions from data. Then you will learn about the classification and regression techniques such as logistic regression, k-NN classifier, and SVM, and implement them in real-world scenarios such as predicting house prices and the number of TV show viewers.

By the end of this course, you will be able to apply the concepts of classification and regression using Python and implement them in a real-world setting.

  • Understand the basic data mining concepts to implement efficient models using Python
  • Know how to use Python libraries and mathematical toolkits such as numpy, pandas, matplotlib, and sci-kit learn
  • Build your first application that makes predictions from data and see how to evaluate the regression model
  • Analyze and implement Logistic Regression and the KNN model
  • Dive into the most effective data cleaning process to get accurate results
  • Master the classification concepts and implement the various classification algorithms

Who this book is written for:

This is an ideal course for the beginners who are willing to step into data mining field.

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Python Data Analysis Cookbook

By Ivan Idris

ISBN 13: 9781785282287 Packt Publishing 462 pages (July 2016)

Book overview:

Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.

By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.

  • Set up reproducible data analysis
  • Clean and transform data
  • Apply advanced statistical analysis
  • Create attractive data visualizations
  • Web scrape and work with databases, Hadoop, and Spark
  • Analyze images and time series data
  • Mine text and analyze social networks
  • Use machine learning and evaluate the results
  • Take advantage of parallelism and concurrency

Who this book is written for:

This book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed.

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Mastering Social Media Mining with Python

By Marco Bonzanini

ISBN 13: 9781783552016 Packt Publishing 338 (July 2016)

Book overview:

Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights. This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data. * Interact with a social media platform via their public API with Python

  • Store social data in a convenient format for data analysis
  • Slice and dice social data using Python tools for data science
  • Apply text analytics techniques to understand what people are talking about on social media
  • Apply advanced statistical and analytical techniques to produce useful insights from data
  • Build beautiful visualizations with web technologies to explore data and present data products

Who this book is written for:

This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data.

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Advanced Machine Learning with Python

By John Hearty

ISBN 13: 9781784398637 Packt Publishing 278 (July 2016)

Book overview:

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms

  • Apply your new found skills to solve real problems, through clearly-explained code for every technique and test
  • Automate large sets of complex data and overcome time-consuming practical challenges
  • Improve the accuracy of models and your existing input data using powerful feature engineering techniques
  • Use multiple learning techniques together to improve the consistency of results
  • Understand the hidden structure of datasets using a range of unsupervised techniques
  • Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach
  • Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together

Who this book is written for:

This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you’ve ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful.

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Python Machine Learning Blueprints

By Alexander T. Combs

ISBN 13: 9781784394752 Packt Publishing 332 (July 2016)

Book overview:

Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice. You’ll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling. That way you’re never left floundering in theory – you’ll be simply collecting and analyzing data in a way that makes a real impact. * Explore and use Python's impressive machine learning ecosystem

  • Successfully evaluate and apply the most effective models to problems
  • Learn the fundamentals of NLP - and put them into practice
  • Visualize data for maximum impact and clarity
  • Deploy machine learning models using third party APIs
  • Get to grips with feature engineering

Who this book is written for:

Python programmers and data scientists - put your skills to the test with this practical guide dedicated to real-world machine learning that makes a real impact.

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Machine Learning for the Web

By Andrea Isoni

ISBN 13: 9781785886607 Packt Publishing 298 (July 2016)

Book overview:

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python’s impressive Django framework and will find out how to build a modern simple web app with machine learning features. * Get familiar with the fundamental concepts and some of the jargons used in the machine learning community

  • Use tools and techniques to mine data from websites
  • Grasp the core concepts of Django framework
  • Get to know the most useful clustering and classification techniques and implement them in Python
  • Acquire all the necessary knowledge to build a web application with Django
  • Successfully build and deploy a movie recommendation system application using the Django framework in Python

Who this book is written for:

The book is aimed at upcoming and new data scientists who have little experience with machine learning or users who are interested in and are working on developing smart (predictive) web applications. Knowledge of Django would be beneficial. The reader is expected to have a background in Python programming and good knowledge of statistics.

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Effective Python Penetration Testing

By Rejah Rehim

ISBN 13: 9781785280696 Packt Publishing 164 (June 2016)

Book overview:

Penetration testing is a practice of testing a computer system, network or web application to find weaknessess in security that an attacker can exploit. Effective Python Penetration Testing will help you utilize your Python scripting skills to safeguard your networks from cyberattacks. We will begin by providing you with an overview of Python scripting and penetration testing. You will learn to analyze network traffic by writing Scapy scripts and will see how to fingerprint web applications with Python libraries such as ProxMon and Spynner. Moving on, you will find put how to write basic attack scripts, and will develop debugging and reverse engineering skills with Python libraries. Towards the end of the book, you will discover how to utilize cryptography toolkits in Python and how to automate Pthon tools and libraries.

  • Write Scapy scripts to investigate network traffic
  • Get to know application fingerprinting techniques with Python
  • Understand the attack scripting techniques
  • Write fuxxing tools with pentesting requirements
  • Learn basic attack scripting methods
  • Utilize cryptographic toolkits in Python

Who this book is written for:

This book is ideal for those who are comfortable with Python or a similar language and need no help with basic programming concepts, but want to understand the basics of penetration testing and the problems pentesters face.

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Mastering Natural Language Processing with Python

By Deepti Chopra, Nisheeth Joshi, Iti Mathur

ISBN 13: 9781783989041 Packt Publishing 238 pages (June 2016)

Book overview:

This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK. You will sequentially be guided through applying machine learning tools to develop various models. We’ll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.

  • Implement string matching algorithms and normalization techniques
  • Implement statistical language modeling techniques
  • Get an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generator
  • Develop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approach
  • Familiarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithm
  • Develop an NER-based system and understand and apply the concepts of sentiment analysis
  • Understand and implement the concepts of Information Retrieval and text summarization
  • Develop a Discourse Analysis System and Anaphora Resolution based system

Who this book is written for:

This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.

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Mastering Python Data Analysis

By Magnus Vilhelm Persson, Luiz Felipe Martins

ISBN 13: 9781783553297 Packt Publishing 284 pages (June 2016)

Book overview:

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.

  • Read, sort and map various data into Python and Panda
  • Recognise patterns so you can understand and explore data
  • Use statistical models to discover patterns in data
  • Review classical statistical inference using Python, Pandas, and SciPy

  • Detect similarities and differences in data with clustering
  • Clean your data to make it useful
  • Work in Jupyter Notebook to produce publication ready figures to be included in reports

Who this book is written for:

This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.

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Python: Real-World Data Science

By Dusty Phillips

ISBN 13: 9781786465160 Packt Publishing 1255 pages (June 2016)

Course overview:

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module.

  • Install and setup Python.
  • Implement objects in Python by creating classes and defining methods.
  • Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis.

  • Create effective visualizations for presenting your data using Matplotlib.
  • Process and analyze data using the time series capabilities of pandas.
  • Interact with different kind of database systems, such as file, disk format, Mongo, and Redis.
  • Apply data mining concepts to real-world problems.
  • Compute on big data, including real-time data from the Internet.

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Learning Predictive Analytics with Python

By Ashish Kumar

ISBN 13: 9781783983261 Packt Publishing 354 pages (February 2016)

Book overview:

This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. With the balance of both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.

  • Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries.
  • Analyze the result parameters arising from the implementation of Predictive Analytics algorithms.
  • Write Python modules/functions from scratch to execute segments or the whole of these algorithms.
  • Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms.
  • Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy.
  • Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries.
  • Understand the best practices while handling datasets in Python and creating predictive models out of them.

Who this book is written for:

If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite.

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Expert Python Programming – Second Edition

By Michał Jaworski, Tarek Ziadé

ISBN 13: 9781785886850 Packt Publishing 536 pages (May 2016)

Book overview:

The focus of the book is to familiarize you with common conventions, best practices, useful tools and standards used by python professionals on a daily basis when working with code.You will begin with knowing new features in Python 3.5 and quick tricks for improving productivity. Next, you will learn advanced and useful python syntax elements brought to this new version. Using advanced object-oriented concepts and mechanisms available in python, you will learn different approaches to implement metaprogramming. You will learn to choose good names, write packages, and create standalone executables easily. You will also be using some powerful tools such as buildout and vitualenv to release and deploy the code on remote servers for production use. Moving on, you will learn to effectively create Python extensions with C, C++, cython, and pyrex. The important factors while writing code such as code management tools, writing clear documentation, and test-driven development are also covered. You will now dive deeper to make your code efficient with general rules of optimization, strategies for finding bottlenecks, and selected tools for application optimization.

By the end of this book, you will be able to be an expert in writing efficient and maintainable code.

  • Conventions and best practices that are widely adopted in the python community.
  • Package python code effectively for community and production use.
  • Easy and lightweight ways to automate code deployment on remote systems.
  • Improve your code’s quality, reliability, and performance.
  • Write concurrent code in python.

Who this book is written for:

The book would appeal to web developers and Python programmers who want to start using version 3.5 and write code efficiently. Basic knowledge of Python programming is expected.

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Internet of Things with Python

By Gastón C. Hillar

ISBN 13: 9781785881381 Packt Publishing 388 pages (May 2016)

Book overview:

This book lets you stay at the forefront of cutting-edge research on IoT. We'll open up the possibilities using tools that enable you to interact with the world, such as Intel Galileo Gen 2, sensors, and other hardware. You will learn how to read, write, and convert digital values to generate analog output by programming Pulse Width Modulation (PWM) in Python. You will get familiar with the complex communication system included in the board, so you can interact with any shield, actuator, or sensor. Later on, you will not only see how to work with data received from the sensors, but also perform actions by sending them to a specific shield. You'll be able to connect your IoT device to the entire world, by integrating WiFi, Bluetooth, and Internet settings. With everything ready, you will see how to work in real time on your IoT device using the MQTT protocol in python. By the end of the book, you will be able to develop IoT prototypes with Python, libraries, and tools.

  • Prototype and develop IoT solutions from scratch with Python as the programming language.
  • Develop IoT projects with Intel Galileo Gen 2 board along with Python.
  • Work with the different components included in the boards using Python and the MRAA library.
  • Interact with sensors, actuators, and shields.
  • Work with UART and local storage.
  • Interact with any electronic device that supports the I2C bus.
  • Allow mobile devices to interact with the board.
  • Work with real-time IoT and cloud services.

Who this book is written for:

The book is ideal for Python developers who want to explore the tools in the Python ecosystem in order to build their own IoT applications and work on IoT-related projects. It is also a very useful resource for developers with experience in other programming languages that want to easily prototype IoT applications with the Intel Galileo Gen 2 board.

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Python Geospatial Development - Third Edition

By Erik Westra

ISBN 13: 9781785288937 Packt Publishing 446 pages (May 2016)

Book overview:

This book provides an overview of the major geospatial concepts, data sources, and toolkits. It starts by showing you how to store and access spatial data using Python, how to perform a range of spatial calculations, and how to store spatial data in a database. Further on, the book teaches you how to build your own slippy map interface within a web application, and finishes with the detailed construction of a geospatial data editor using the GeoDjango framework.By the end of this book, you will be able to confidently use Python to write your own geospatial applications ranging from quick, one-off utilities to sophisticated web-based applications using maps and other geospatial data.

  • Access, manipulate, and display geospatial data from within your Python programs.
  • Master the core geospatial concepts of location, distance, units, projections, and datums.
  • Read and write geospatial data in both vector and raster format.
  • Perform complex, real-world geospatial calculations using Python.
  • Store and access geospatial information in a database.
  • Use points, lines, and polygons within your Python programs.
  • Convert geospatial data into attractive maps using Python-based tools.

Who this book is written for:

This book is for experienced Python developers who want to learn about geospatial concepts, obtain and work with geospatial data, solve spatial problems, and build sophisticated map-based applications using Python.

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Mastering IPython 4.0

By Thomas Bitterman

ISBN 13: 9781785888410 Packt Publishing 382 pages (May 2016)

Book overview:

This book will get IPython developers up to date with the latest advancements in IPython and dive deep into interactive computing with IPython. This an advanced guide on interactive and parallel computing with IPython will explore advanced visualizations and high-performance computing with IPython in detail. You will quickly brush up your knowledge of IPython kernels and wrapper kernels, then we'll move to advanced concepts such as testing, Sphinx, JS events, interactive work, and the ZMQ cluster. The book will cover topics such as IPython Console Lexer, advanced configuration, and third-party tools.

  • Develop skills to use IPython for high performance computing (HPC).
  • Understand the IPython interactive shell.
  • Use XeroMQ and MPI to pass messages.
  • Integrate third-party tools like R, Julia, and JavaScript with IPython.

  • Visualize the data.
  • Acquire knowledge to test and document the data.

Who this book is written for:

This book is for IPython deelopers who want to make the most of IPython and perform advanced scientific computing with IPython utilizing the ease of interactive computing. It is ideal for users who wish to learn about the interactive and parallel computing properties of IPython 4.0, along with its integration with third-party tools and concepts such as testing and documenting results.

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Practical Machine Learning

By Sunila Gollapudi

ISBN 13: 9781784399689 Packt Publishing 468 pages (January 2016)

Book overview:

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data.

  • Implement a wide range of algorithms and techniques for tackling complex data.
  • Get to grips with some of the most powerful languages in data science, including R, Python, and Julia.
  • Harness the capabilities of Spark and Hadoop to manage and process data successfully.
  • Apply the appropriate machine learning technique to address real-world problems.
  • Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning.

Who this book is written for:

This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.

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Mastering Python

By Rick van Hattem

ISBN 13: 9781785289729 Packt Publishing 486 pages (April 2016)

Book overview:

This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers.

  • Create a virtualenv and start a new project
  • Understand how and when to use the functional programming paradigm
  • Get familiar with the different ways the decorators can be written in
  • Understand the power of generators and coroutines without digressing into lambda calculus
  • Create metaclasses and how it makes working with Python far easier
  • Generate HTML documentation out of documents and code using Sphinx
  • Learn how to track and optimize application performance, both memory and cpu
  • Use the multiprocessing library, not just locally but also across multiple machines

Who this book is written for:

Almost anyone can learn to write working script and create high quality code but they might lack a structured understanding of what it means to be 'Pythonic'. If you are a Python programmer who wants to code efficiently by getting the syntax and usage of a few intricate Python techniques exactly right, this book is for you.

Publisher's page


Distributed Computing with Python

By Francesco Pierfederici

ISBN 13: 9781785889691 Packt Publishing 170 pages (April 2016)

Book overview:

This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task.

  • Get an introduction to parallel and distributed computing
  • See synchronous and asynchronous programming
  • Explore parallelism in Python
  • Distributed application with Celery
  • Python in the Cloud
  • Python on an HPC cluster
  • Test and debug distributed applications

Who this book is written for:

This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks.

Publisher's page


Designing Machine Learning Systems with Python

By David Julian

ISBN 13: 9781785882951 Packt Publishing 232 pages (April 2016)

Book overview:

  • Gain an understanding of the machine learning design process
  • Optimize machine learning systems for improved accuracy
  • Understand common programming tools and techniques for machine learning
  • Develop techniques and strategies for dealing with large amounts of data from a variety of sources
  • Build models to solve unique tasks

Who this book is written for:

This book is for data scientists, scientists, or just the curious.

Publisher's page


Flask By Example

By Gareth Dwyer

ISBN 13: 9781785286933 Packt Publishing 276 Pages (March 2016)

Book overview:

  • Build three web applications from the ground up using the powerful Python micro framework, Flask.
  • Dynamically display data to your viewers, based on their requests.
  • Store user and static data in SQL and NoSQL databases and use this data to power your web applications.
  • Create a good user experience by combining HTML, CSS, and JavaScript.

  • Harness the convenience of freely available APIs, including OpenWeatherMap, Open Exchange Rates, and bitly.

  • Extend your applications to build advanced functionality, such as a user account control system using Flask-Login.
  • Learn about web application security and defend against common attacks, such as SQL injection and XSS.

Who this book is for

This book is aimed at developers and hobbyists who have some knowledge of Python but no knowledge of the micro-framework Flask.

Publisher's page


Learning Python Web Penetration Testing -Video Course

By Christian Martorella

ISBN 13: 9781785280351 Packt Publishing 2 hours and 50 minutes (March 2016)

Video Course overview:

  • Understand the web application penetration testing methodology and toolkit.
  • Interact with web applications using Python and the Requests library.
  • Intercept and manipulate HTTP communication using Mitmproxy.

Who this video course is for

This video is for web developers who want to step into the web application security testing world. Familiarity with Python is essential, but not to an expert level.

Publisher's page


Learning Cython Programming - Second Edition

By Philip Herron

ISBN 13: 9781783551675 Packt Publishing 110 pages (February 2016)

Book overview:

This new edition of Learning Cython Programming shows you how to get started, taking you through the fundamentals so you can begin to experience its unique powers.

  • Reuse Python logging in C
  • Make an IRC bot out of your C application
  • Extend an application so you have a web server for rest calls
  • Practice Cython against your C++ code
  • Discover tricks to work with Python ConfigParser in C

  • Create Python bindings for native libraries
  • Find out about threading and concurrency related to GIL
  • Expand Terminal Multiplexer Tmux with Cython

Who this book is for

This book is for developers who are familiar with the basics of C and Python programming and wish to learn Cython programming to extend their applications.

Publisher's page


Web API Development with Flask-Video Course

By Gergo Bogdan

ISBN 13: 9781783551750 Packt Publishing 1 hour and 40 minutes (February 2016)

Video Course overview:

  • Understand the fundamental capabilities of the Flask framework.
  • You will learn how to test APIs written in Python with the support of Flask.
  • Design and develop large applications independently from Flask.

Who this video course is for

This video is for web developers who want to build RESTful web APIs using Flask with Python.Developers should be familiar with Python and basic web concepts, such as HTTP verbs and JSON, and should possess basic database knowledge in order to understand SQLAlchemy concepts.

Publisher's page


Regression Analysis with Python

By Luca Massaron, Alberto Boschetti

ISBN 13: 9781785286315 Packt Publishing 312 pages (February 2016)

Book overview:

  • Apply multiple linear regression to real-world problems.
  • Create an observation matrix, using different techniques of data analysis and cleaning.
  • Learn to scale linear models to a big dataset and deal with incremental data.

Who this book is written for

The book targets Python developers, with a basic understanding of data science, statistics, and math, who want to learn how to do regression analysis on a dataset. It is beneficial if you have some knowledge of statistics and data science.

Publisher's page


Deep Learning with Python-Video Course

By Eder Santana

ISBN 13: 9781785883873 Packt Publishing 1 Hour 45 Minutes (February 2016)

Video Course overview:

  • Learn the fundamentals of Machine Learning and build your own intelligent applications.
  • Implement automatic image recognition and text analysis models using Deep learning.
  • Get to know each concept along with its practical implementation.

Who this video course is for

This course is for developers looking for free, open source deep learning solutions for media (image and text) classification. It is also aimed for senior undergrad and first year grad students beginning in the field of Deep Learning.

Publisher's page


Learning Predictive Analytics with Python

By Ashish Kumar

ISBN 13: 9781783983261 Packt Publishing 354 pages (February 2016)

Book overview:

  • A step-by-step guide to predictive modeling.
  • Get to grips with the basics of Predictive Analytics with Python.
  • Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering.

Who this book is written for

If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you

Publisher's page


Learning Python Design Patterns - Second Edition

By Chetan Giridhar

ISBN 13: 9781785888038 Packt Publishing 164 pages (February 2016)

Book overview:

  • Implement real-world scenarios with Python’s latest release, Python v3.5.
  • Learn about Singleton patterns, Factory patterns, and Façade patterns in detail.
  • Enhance your professional abilities in software architecture, design, and development.

Who this book is written for

This book is written for intermediate Python programmers.

Publisher's page


PySide GUI Application Development - Second Edition

By Gopinath Jaganmohan, Venkateshwaran Loganathan

ISBN 13: 9781785282454 Packt Publishing 144 pages (January 2016)

Book overview:

  • Designed for beginners to help you get started with GUI application development.
  • Develop your own applications by creating customized widgets and dialogues.
  • Written in a simple and elegant structure so you easily understand how to program various GUI components.

Who this book is written for

This book is written for Python programmers who want to learn about GUI programming.

Publisher's page


Geospatial Development By Example with Python

By Pablo Carreira

ISBN 13: 9781785282355 Packt Publishing 340 pages (January 2016)

Book overview:

  • Learn the full geo-processing workflow using Python with open source packages
  • Create press-quality styled maps and data visualization with high-level and reusable code
  • Process massive datasets efficiently using parallel processing

Who this book is written for

Geospatial Development By Example with Python is intended for beginners or advanced developers in Python who want to work with geographic data.

Publisher's page


Getting Started with Python and Raspberry Pi

By Dan Nixon

ISBN 13: 9781783551590 Packt Publishing 200 pages (September 2015)

Book overview:

  • Learn the fundamentals of Python scripting and application programming
  • Design user-friendly command-line and graphical user interfaces
  • A step-by-step guide to learning Python programming with the Pi

Who this book is written for

This book is designed for those who are unfamiliar with the art of Python development and want to get to know their way round the language and the many additional libraries that allow you to get a full application up and running in no time.

Publisher's page


Learning Python

By Fabrizio Romano

ISBN 13: 9781783551712 Packt Publishing 442 pages (December 2015)

Book overview:

  • Learn the fundamentals of programming with Python – one of the best languages ever created
  • Develop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Python’s portability
  • Create outstanding applications of all kind, from websites to scripting, and from GUIs to data science

Who this book is written for

Python is the most popular introductory teaching language in U.S. top computer science universities, so if you are new to software development, or maybe you have little experience, and would like to start off on the right foot, then this language and this book are what you need.

Publisher's page


Learning Geospatial Analysis with Python - Second Edition

By Joel Lawhead

ISBN 13: 9781783552429 Packt Publishing 394 pages (December 2015)

Book overview:

  • Construct applications for GIS development by exploiting Python
  • This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system—no compiling of C libraries necessary
  • This practical, hands-on tutorial teaches you all about Geospatial analysis in Python

Who this book is written for

If you are a Python developer, researcher, or analyst who wants to perform Geospatial, modeling, and GIS analysis with Python, then this book is for you.

Publisher's page


Python Unlocked

By Arun Tigeraniya

ISBN 13: 9781785885990 Packt Publishing 172 pages (December 2015)

Book overview:

  • Write smarter, bug-free, high performance code with minimal effort
  • Uncover the best tools and options available to Python developers today
  • Deploy decorators, design patters, and various optimization techniques to use Python 3.5 effectively

Who this book is written for

If you are a Python developer and you think that you don’t know everything about the language yet, then this is the book for you.

Publisher's page


wxPython Application Development Cookbook

By Cody Precord

ISBN 13: 9781785287732 Packt Publishing 264 pages (December 2015)

Book overview:

  • This book empowers you to create rich cross-platform graphical user interfaces using Python
  • It helps you develop applications that can be deployed on Windows, OSX, and Linux
  • The recipes in the book involve real-world applications, giving you a first-hand experience of the practical scenarios

Who this book is written for

For those who are familiar with programming in Python and want to start building applications with graphical user interfaces, this book will get you up and running quickly.

Publisher's page


Spark for Python Developers

By Amit Nandi

ISBN 13: 9781784399696 Packt Publishing 206 pages (December 2015)

Book overview:

  • Set up real-time streaming and batch data intensive infrastructure using Spark and Python
  • Deliver insightful visualizations in a web app using Spark (PySpark)

  • Inject live data using Spark Streaming with real-time events

Who this book is written for

This book is for data scientists and software developers with a focus on Python who want to work with the Spark engine, and it will also benefit Enterprise Architects.

Publisher's page


Python Business Intelligence Cookbook

By Robert Dempsey

ISBN 13: 9781785287466 Packt Publishing 202 pages (December 2015)

Book overview:

  • Want to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practical, easy-to-follow guide
  • Learn to apply Python for business intelligence tasks—preparing, exploring, analyzing, visualizing and reporting—in order to make more informed business decisions using data at hand
  • Learn to explore and analyze business data, and build business intelligence dashboards with the help of various insightful recipes

Who this book is written for

This book is intended for data analysts, managers, and executives with a basic knowledge of Python, who now want to use Python for their BI tasks.

Publisher's page


Building Python Real time Applications with Storm

By Kartik Bhatnagar, Barry Hart

ISBN 13: 9781784392857 Packt Publishing 122 pages (December 2015)

Book overview:

  • Learn to use Apache Storm and the Python Petrel library to build distributed applications that process large streams of data
  • Explore sample applications in real-time and analyze them in the popular NoSQL databases MongoDB and Redis
  • Discover how to apply software development best practices to improve performance, productivity, and quality in your Storm projects

Who this book is written for

This book is intended for Python developers who want to benefit from Storm’s real-time data processing capabilities.

Publisher's page


Python GUI Programming Cookbook

By Burkhard A. Meier

ISBN 13: 9781785283758 Packt Publishing 350 pages (December 2015)

Book overview:

  • Use object-oriented programming to develop amazing GUIs in Python
  • Create a working GUI project as a central resource for developing your Python GUIs
  • Packed with easy-to-follow recipes to help you develop code using the latest released version of Python

Who this book is written for

If you are a Python programmer with intermediate level knowledge of GUI programming

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