ISBN 13: 9781789138726 Packt 420 Pages (October 2018)
A comprehensive guide to help you understand the principles of Reactive and asynchronous programming and its benefits
Reactive programming is central to many concurrent systems, but it’s famous for its steep learning curve, which makes most developers feel like they're hitting a wall. With this book, you will get to grips with reactive programming by steadily exploring various concepts
This hands-on guide gets you started with Reactive Programming (RP) in Python. You will learn abouta the principles and benefits of using RP, which can be leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming (FaRP), observables and observers, and concurrency and parallelism. The book will then take you through the implementation of an audio transcoding server and introduce you to a library that helps in the writing of FaRP code. You will understand how to use third-party services and dynamically reconfigure an application.
By the end of the book, you will also have learned how to deploy and scale your applications with Docker and Traefik and explore the significant potential behind the reactive streams concept, and you'll have got to grips with a comprehensive set of best practices.
Daniel Gaspar, Jack Stouffer
ISBN 13: 9781788995405 Packt 332 Pages (October 2018)
Learn to build modern, secure, highly available web MVC applications and API’s using Python`s Flask framework.
Flask is a popular Python framework known for its lightweight and modular design. Mastering Flask Web Development will take you on a complete tour of the Flask environment and teach you how to build a production-ready application.
You'll begin by learning about the installation of Flask and basic concepts such as MVC and accessing a database using an ORM. You will learn how to structure your application so that it can scale to any size with the help of Flask Blueprints. You'll then learn how to use Jinja2 templates with a high level of expertise. You will also learn how to develop with SQL or NoSQL databases, and how to develop REST APIs and JWT authentication. Next, you'll move on to build role-based access security and authentication using LDAP, OAuth, OpenID, and database. Also learn how to create asynchronous tasks that can scale to any load using Celery and RabbitMQ or Redis. You will also be introduced to a wide range of Flask extensions to leverage technologies such as cache, localization, and debugging. You will learn how to build your own Flask extensions, how to write tests, and how to get test coverage reports. Finally, you will learn how to deploy your application on Heroku and AWS using various technologies, such as Docker, CloudFormation, and Elastic Beanstalk, and will also learn how to develop Jenkins pipelines to build, test, and deploy applications.
Eduonix Learning Solutions
ISBN 13: 9781789536591 Packt 134 Pages (October 2018)
Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn
Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics.
This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks.
ISBN 13: 9781789343700 Packt 172 Pages (October 2018)
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.
This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.
Matthew Lamons, Rahul Kumar, Abhishek Nagaraja
ISBN 13: 9781788997096 Packt 472 Pages (October 2018)
Insightful projects to master deep learning and neural network architectures using Python and Keras
Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.
Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.
José Manuel Ortega
ISBN 13: 9781788992510 Packt 426 Pages (September 2018)
Master Python scripting to build a network and perform security operations
Python is being used for increasingly advanced tasks, with the latest update introducing many new packages. This book focuses on leveraging these updated packages to build a secure network with the help of Python scripting.
This book covers topics from building a network to the different procedures you need to follow to secure it. You’ll first be introduced to different packages and libraries, before moving on to different ways to build a network with the help of Python scripting. Later, you will learn how to check a network’s vulnerability using Python security scripting, and understand how to check vulnerabilities in your network. As you progress through the chapters, you will also learn how to achieve endpoint protection by leveraging Python packages along with writing forensic scripts. By the end of this book, you will be able to get the most out of the Python language to build secure and robust networks that are resilient to attacks.
ISBN 13: 9781789133806 Packt 398 Pages (September 2018)
Step-by-step instructions which take you through each program to automate monotonous tasks with Python 3.7
The Python Automation Cookbook helps you develop a clear understanding of how to automate your business processes using Python, including detecting opportunities by scraping the web, analyzing information to generate automatic spreadsheets reports with graphs, and communicating with automatically generated emails.
You’ll learn how to get notifications via text messages and run tasks while your mind is focused on other important activities, followed by understanding how to scan documents such as résumés. Once you’ve gotten familiar with the fundamentals, you’ll be introduced to the world of graphs, along with studying how to produce organized charts using Matplotlib. In addition to this, you’ll gain in-depth knowledge of how to generate rich graphics showing relevant information.
ISBN 13: 9781789617740 Packt 154 Pages (September 2018)
Learn advanced techniques to improve the performance and quality of your predictive models
Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.
This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.
By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.
ISBN 13: 9781788398343 Packt Course Length: 2 hours 53 minutes (September 2018)
Create simple binary exploits with Python
This video course starts with high-level code injection, the simplest sort of exploit. It then explains binary exploits that allow you to skip past unwanted code, such as the password or product key tests, and add Trojan code. You will perform the exploit development process: finding a vulnerability, analyzing a crash in a debugger, creating a crafted attack, and achieving remote code execution on Windows and Linux. You will use the gdb debugger to analyze Linux executables and Python code to exploit them. On Windows, you'll use the Immunity debugger and Python.
This course uses a demonstration-and-challenge approach which guides students from passive observation to practical implementation of binary exploit at development techniques.
Sean Saito, Yang Wenzhuo and Rajalingappaa Shanmugamani
ISBN 13: 9781788991612 Packt 296 Pages (September 2018)
Deploy autonomous agents in business systems using powerful Python libraries and sophisticated reinforcement learning models
Reinforcement learning (RL) is the next big leap in the artificial intelligence domain, given that it is unsupervised, optimized, and fast. Python Reinforcement Learning Projects takes you through various aspects and methodologies of reinforcement learning, with the help of insightful projects.
You will learn about core concepts of reinforcement learning, such as Q-learning, Markov models, the Monte-Carlo process, and deep reinforcement learning. As you make your way through the book, you’ll work on projects with various datasets, including numerical, text, video, and audio, and will gain experience in gaming, image rocessing, audio processing, and recommendation system projects. You’ll explore TensorFlow and OpenAI Gym to implement a deep learning RL agent that can play an Atari game. In addition to this, you will learn how to tune and configure RL algorithms and parameters by building agents for different kinds of games. In the concluding chapters, you’ll get to grips with building self-learning models that will not only uncover layers of data but also reason and make decisions.
By the end of this book, you will have created eight real-world projects that explore reinforcement learning and will have handson experience with real data and artificial intelligence (AI) problems.
Ankur Ankan and Abinash Panda
ISBN 13: 9781788625449 Packt 178 Pages (September 2018)
Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn
Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.
Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.
In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.
By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.
Deepti Chopra, Iti Mathur, Nisheeth Joshi
ISBN 13: 9781789618358 Packt Course Length: 1 hour 37 minutes (August 2018 )
Explore various NLP tasks while enhancing your Python skills in real-world scenarios!
This course 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, Text Summarization, and Anaphora Resolution.
ISBN 13: 9781789347654 Packt Course Length: 3 hours 6 minutes (August 2018)
Teach your machine to think for itself!
This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning.
By the end of the video course, you’ll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems.
ISBN 13: 9781789131222 Packt Course Length: 2 hours 34 minutes (August 2018)
Predict heart disease, customer-buying behaviors, and much more in this course filled with real-world projects
Scikit-Learn is one of the most powerful Python Libraries with has a clean API, and is robust, fast and easy to use. It solves real-world problems in the areas of health, population analysis, and figuring out buying behavior, and more.
In this course you will build powerful projects using Scikit-Learn. Using algorithms, you will learn to read trends in the market to address market demand. You'll delve more deeply to decode buying behavior using Classification algorithms; cluster the population of a place to gain insights into using K-Means Clustering; and create a model using Support Vector Machine classifiers to predict heart disease.
ISBN 13: 9781789138580 Packt Course Length: 1 hour 47 minutes (August 2018)
Use Selenium automation, to save you time when testing in Python
This course introduces Python users to Selenium WebDriver. It will help users with basic programming knowledge to start code-testing. This course will help users cut down on their time spent on testing code written in Python.
ou will then start practicing the basics. Next, you will explore the biggest strength of Selenium: integrating your test instances with the browser of your choice. You will then actually test an authentication system in a sequential manner by following each of the required steps. By the end of the course, you will be able to provide your customers and clients with better quality code in less time, enhancing customer satisfaction!
ISBN 13: 9781789531701 Packt 178 Pages (August 2018)
Enhance your data analysis and predictive modeling skills using popular Python tools
In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.
By the end of this book, you will have hands-on experience performing data analysis with Python.
Harish Kumar Garg
ISBN 13: 9781789537000 Packt 90 Pages (August 2018)
Simplified Python programming for Bitcoin and Blockchain
Bitcoin is a cryptocurrency that’s changing the face of online payments. Hands-On Bitcoin Programming with Python teaches you to build software applications for mining and creating Bitcoins using Python.
This book starts with the basics of both Bitcoin and blockchain and gives you an overview of these inherent concepts by showing you how to build Bitcoin-driven applications with Python. Packed with clear instructions and practical examples, you will learn to understand simple Python coding examples that work with this cryptocurrency.
Alex Galea and Luis Capelo
ISBN 13: 9781789804744 Packt 334 Pages (August 2018)
A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples
aking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work.
We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data.
ISBN 13: 9781788830829 Packt 366 Pages (August 2018)
Build optimized applications in Python by smartly implementing the standard library
The Python 3 Standard Library is a vast array of modules that you can use for developing various kinds of applications. It contains an exhaustive list of libraries, and this book will help you choose the best one to address specific programming problems in Python.
The Modern Python Standard Library Cookbook begins with recipes on containers and data structures and guides you in performing effective text management in Python. You will find Python recipes for command-line operations, networking, filesystems and directories, and concurrent execution. You will learn about Python security essentials in Python and get to grips with various development tools for debugging, benchmarking, inspection, error reporting, and tracing. The book includes recipes to help you create graphical user interfaces for your application. You will learn to work with multimedia components and perform mathematical operations on date and time. The recipes will also show you how to deploy different searching and sorting algorithms on your data.
ISBN 13: 9781788835831 Packt 332 Pages (August 2018)
Getting the most out of Python to improve your codebase
The book begins by describing the basic elements of writing clean code and how it plays an important role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. You will learn to implement the SOLID principles in Python and use decorators to improve your code. The book delves more deeply into object oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve software problems by implementing design patterns in your code. In the final chapter we break down a monolithic application to a microservice one, starting from the code as the basis for a solid platform.
By the end of the book, you will be proficient in applying industry approved coding practices to design clean, sustainable and readable Python code.
ISBN 13: 9781788835794 Packt Course Length: 2 hours 13 minutes (August 2018)
Optimize Pycharm's strength to develop application easily.
By the end of the course, you’ll be well-versed with a terrific tool for Python development—PyCharm—and be able to do everything expected of a Python developer.
ISBN 13: 9781789347562 Packt 160 Pages (August 2018)
Learn the pytest way to write simple tests which can also be used to write complex tests
Python's standard unittest module is based on the xUnit family of frameworks, which has its origins in Smalltalk and Java, and tends to be verbose to use and not easily extensible.The pytest framework on the other hand is very simple to get started, but powerful enough to cover complex testing integration scenarios, being considered by many the true Pythonic approach to testing in Python.
In this book, you will learn how to get started right away and get the most out of pytest in your daily workﬂow, exploring powerful mechanisms and plugins to facilitate many common testing tasks. You will also see how to use pytest in existing unittest-based test suites and will learn some tricks to make the jump to a pytest-style test suite quickly and easily.
ISBN 13: 9781789135992 Packt 466 Pages (August 2018)
Master the art of using Python for a diverse range of network engineering tasks
Networks in your infrastructure set the foundation for how your application can be deployed, maintained, and serviced. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In this second edition of Mastering Python Networking, you’ll embark on a Python-based journey to transition from traditional network engineers to network developers ready for the next-generation of networks.
This book begins by reviewing the basics of Python and teaches you how Python can interact with both legacy and API-enabled network devices. As you make your way through the chapters, you will then learn to leverage high-level Python packages and frameworks to perform network engineering tasks for automation, monitoring, management, and enhanced security. In the concluding chapters, you will use Jenkins for continuous network integration as well as testing tools to verify your network.
Rajesh Arumugam, Rajalingappaa Shanmugamani
ISBN 13: 9781789139495 Packt 312 Pages (July 2018)
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow
Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.
To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow.
ISBN 13: 9781788993753 Packt 146 Pages (July 2018)
With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web
This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible..
In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques
With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.
ISBN 13: 9781788472784 Packt Course Length: 3 hours 16 minutes (July 2018)
Learn Python like a Professional! Start from the basics and go all the way to create your program in it.
In this course, you will start by setting up your development environment, including downloading Python and setting up your IDE (PyCharm); you'll then be introduced to Python lists and list comprehensions. The course will then show you what a generator is and why you might want to use one. Further on, you will be introduced to functions and decorators, and see how you can use them. You will then learn how to create a very simple, single-file Python program, and how to execute it both from the command line and from within the IDE. You'll also get a very brief introduction to debugging.
By the end of the course, you'll have learned how to manipulate strings, parsing and printing them.
ISBN 13: 9781788293143 Packt Course Length: 2 hours 49 minutes (July 2018)
Take your skills with Python to the next level by guiding you to pick the right stack.
With the growing demand for full-stack developers, it is important to learn how you can combine the powers of Python with other libraries to create amazing applications.
Django has basic front-end support but we show how complex operations can be performed at the front-end. We show you how to unleash the power of Python's Rest API and other functionalities to create compelling applications powered by ReactJS.
We will be using a production-level database such as Postgres. Delving into key aspects such as code reusability, deployment and maintaining your application, we discuss production server configuration and bundle technologies with Python to provide an end-to-end web development solution.
ISBN 13: 9781789138535 Packt Course Length: 2 hours and 38 minutes (July 2018)
From first-class functions to abstract base classes, apply Pythonic tricks to your application and be a Python rockstar
This course is will take you from a Python outsider to an insider. You will benefit from insights from the Python documentation, PEPs, and online developer communities to learn the ultimate Pythonic ways to tackle common programming patterns. This course covers tips, tricks, and techniques for loops, data structures, object-oriented programming, functions, and more, helping you work on ordered collections and key-value stores for dictionaries. You will be able to increase the speed and performance of your code while making it easier to debug. Start writing cleaner code for your applications and learn to organize it better in just 3 hours.
he course is full of hands-on instructions, interesting and illustrative visualizations, and clear explanations. It is packed full of useful tips and relevant advice which can be applied to real, commercial products. Throughout the course, we maintain a focus on practicality and getting things done, not fancy programming concepts and theory.
ISBN 13: 9781789139235 Packt Course Length: 3 hours 17 minutes (July 2018)
Improve your application development process to build efficient apps with Python
The course starts by dealing with performance issues and learning how to tackle them in an application. Distributing an application with Python is not easy but you will learn ways to distribute applications developed using Python along with GUIs, web applications, and more. Further, you will learn to test your application at different levels and use modern software at the development stage. You will also learn to expand productivity using standard and third-party tools. Finally, you will learn to protect your code quality from potential errors and detect any problem at an early stage, to prevent any issues later.
You will start by exploring Python issues and how to diagnose them. Then you will be introduced to the most popular ways to distribute Python applications. Next, you will test applications and use modern software in the development process. Finally, you will learn to expand your productivity and manage code quality to prevent any issues later.
ISBN 13: 9781788995337 Packt Course Length: 2 hours 50 minutes (July 2018)
Tackle Python performance problems and speed up your apps with parallelism, concurrent execution, and OOP
Troubleshooting Python Application Development is your answer. This course takes you through a structured journey of performance problems that your application is likely to encounter, and presents both the intuition and the solution to these issues. You'll get things done, without a lengthy detour into how Python is implemented or computational theory.
The course is full of hands-on instructions, interesting and illustrative visualizations, and, clear explanations from a data scientist. It is packed full of useful tips and relevant advice. Throughout the course, we maintain a focus on practicality and getting things done, not fancy mathematical theory.
ISBN 13: 9781788994583 Packt XXX Pages OR Course Length: 3 hours 3 minutes (July 2018)
The complete guide that will help you create and automate your network with Python
The goal of the course is to master key Python concepts as they are applied in the network engineering context. Computer Networks, large and small, has become ever increasingly software-driven in the last several years. A typical network engineer faces the challenge of learning a completely new discipline that normally takes years to master. This course will use Python, an easy-to-learn, popular first-time-programmer language as the foundation to bridge the gap for network engineers facing the automation challenge, this will help network engineers become experts in using Python for networking.
The video will start by demonstrating how to use Python and supported libraries to automate network tasks. Then you will get familiar with the concepts and work with the Ansible framework in order to achieve your network goals. You will then come across ways to monitor your network using Python. Besides this, you will develop a good understanding and explore ways to solve issues related to the security wheel that treats the network as one single entity instead of dispersing components. By the end of the course, the students will master skills required to accomplish advance network automation using Python.
ISBN 13: 9781789345612 Packt Course Length: 2 hours 46 minutes (July 2018)
A complete Python guide to Natural Language Processing to build spam filters, topic classifiers, and sentiment analyzers
There is an overflow of text data online nowadays. As a Python developer, you need to create a new solution using Natural Language Processing for your next project. Your colleagues depend on you to monetize gigabytes of unstructured text data. What do you do?
Hands-on NLP with NLTK and scikit-learn is the answer. This course puts you right on the spot, starting off with building a spam classifier in our first video. At the end of the course, you are going to walk away with three NLP applications: a spam filter, a topic classifier, and a sentiment analyzer. There is no need for fancy mathematical theory, just plain English explanations of core NLP concepts and how to apply those using Python libraries. Taking this course will help you to precisely create new applications with Python and NLP. You will be able to build actual solutions backed by machine learning and NLP processing models with ease.
Alberto Boschetti, Luca Massaron
ISBN 13: 9781789616057 Packt Course Length: 1 hours 50 minutes (July 2018)
Become an efficient data science practitioner by understanding Python's key concepts
This course will make you 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. We start with SVM and random forest for classification and regression. We look at big data, deep learning, and language processing.
Then we use graph analysis techniques for very interesting and trending social media analytics. Finally, we take a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment tools that make it easier to present your results to an audience of both data science experts and business users.
ISBN 13: 9781789539219 Packt Course Length: 2 hours 38 minutes (July 2018)
Use IPython and Jupyter Notebook to sharpen your skills for your data analysis and visualization tasks
Machine learning and data analysis are the center of attraction for many engineers and scientists. The reason is quite obvious: its vast application in numerous fields and booming career options. And Python is one of the leading open source platforms for data science and numerical computing. IPython, and its associated Jupyter Notebook, provide Python with efficient interfaces to for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. If you are among those seeking to enhance their capabilities in machine learning, then this course is the right choice.
Statistical Methods and Applied Mathematics in Data Science provides many easy-to-follow, ready-to-use, and focused recipes for data analysis and scientific computing. This course tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
ISBN 13: 9781788831000 Packt 462 Pages (July 2018)
Over 60 recipes to help you design interactive, smart, and cross-platform GUI applications
PyQt is one of the best cross-platform interface toolkits currently available; it's stable, mature, and completely native. If you want control over all aspects of UI elements, PyQt is what you need. This book will guide you through every concept necessary to create fully functional GUI applications using PyQt, with only a few lines of code.
As you expand your GUI using more widgets, you will cover networks, databases, and graphical libraries that greatly enhance its functionality. Next, the book guides you in using Qt Designer to design user interfaces and implementing and testing dialogs, events, the clipboard, and drag and drop functionality to customize your GUI. You will learn a variety of topics, such as look and feel customization, GUI animation, graphics rendering, implementing Google Maps, and more.
Luis Pedro Coelho, Willi Richert, Matthieu Brucher
ISBN 13: 9781788623223 Packt 406 Pages (July 2018)
Get more from your data by creating practical machine learning systems with Python
This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems.
By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks.
Abdulwahid Abdulhaque Barguzar
ISBN 13: 9781788837613 Packt 324 Pages (July 2018)
Master serverless architectures in Python and their implementation, with Zappa on three different frameworks.
Serverless applications are becoming very popular these days, not just because they save developers the trouble of managing the servers, but also because they provide several other benefits such as cutting heavy costs and improving the overall performance of the application.
This book will help you build serverless applications in a quick and efficient way. We begin with an introduction to AWS and the API gateway, the environment for serverless development, and Zappa. We then look at building, testing, and deploying apps in AWS with three different frameworks--Flask, Django, and Pyramid. Setting up a custom domain along with SSL certificates and configuring them with Zappa is also covered. A few advanced Zappa settings are also covered along with securing Zappa with AWS VPC.
Denny Lee, Tomasz Drabas
ISBN 13: 9781788835367 Packt 330 Pages (June 2018)
Combine the power of Apache Spark and Python to build effective big data applications
You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.
By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
ISBN 13: 9781788836524 Packt 318 Pages (June 2018)
A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python
The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.
By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Umit Mert Cakmak, Mert Cuhadaroglu
ISBN 13: 9781788993357 Packt 248 Pages (June 2018)
Enhance the power of NumPy and start boosting your scientific computing capabilities
Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system.
By the end of this book, you will have become an expert in handling and performing complex data manipulations.
ISBN 13: 9781788996662 Packt 508 Pages (June 2018)
Build a solid foundation in coding by utilizing the language and its core characteristics
You will begin by exploring the foundations of and fundamental topics on Python and learn to manipulate them. Then, you'll explore different programming paradigms that will allow you to find the best approach to a situation, and you’ll also understand how to carry out performance optimization as well as effective debugging. As you make your way through the chapters, you'll control the flow of a program, and persist and utilize an interchange format to exchange data. You'll also walk through cryptographic services in Python and understand secure tokens.
Throughout, the book covers various types of applications, and it concludes with building real-world applications based on all the concepts that you learned. By the end of the book, you'll have a proper understanding of the Python language and a solid grasp on how to work with data. You'll know how to quickly build a website and harness the power of Python's renowned data science libraries.
ISBN 13: 9781788998512 Packt 398 Pages (June 2018)
Invent your own Python scripts to automate your infrastructure
We’ll explore examples of network automation tasks using simple Python programs and Ansible. Next, we will walk you through automating administration tasks with Python Fabric, where you will learn to perform server configuration and administration, along with system administration tasks such as user management, database management, and process management. As you progress through this book, you’ll automate several testing services with Python scripts and perform automation tasks on virtual machines and cloud infrastructure with Python. In the concluding chapters, you will cover Python-based offensive security tools and learn how to automate your security tasks.
By the end of this book, you will have mastered the skills of automating several system administration tasks with Python.
ISBN 13: 9781788997409 Packt 276 Pages (June 2018)
Become a master at penetration testing using machine learning with Python
This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system.
As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system.
By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system
Ashish Singh Bhatia
ISBN 13: 9781788997591 Packt Course Length: 3 hours (June 2018)
Step into the world of PyTorch to create deep learning models with the help of real-world examples
PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.
The course starts with the fundamentals of PyTorch and how to use basic commands. Next, you’ll learn about Convolutional Neural Networks (CNN) through an example of image recognition, where you’ll look into images from a machine perspective.
The next project shows you how to predict character sequence using Recurrent Neural Networks (RNN) and Long Short Term Memory Network (LSTM). Then you’ll learn to work with autoencoders to detect credit card fraud. After that, it’s time to develop a system using Boltzmann Machines, where you’ll recommend whether to watch a movie or not.
We’ll continue with Boltzmann Machines, where you’ll learn to give movie ratings using AutoEncoders. In the end, you’ll get to develop and train a model to recognize a picture or an object from a given image using Deep Learning, where we’ll not only detect the shape, but also the color of the object.
By the end of the course, you’ll be able to start using PyTorch to build Deep Learning models by implementing practical projects in the real world. So, grab this course as it will take you through interesting real-world projects to train your first neural nets.
ISBN 13: 9781788835565 Packt Course Length: 2 hours 19 minutes (June 2018)
Explore Python’s powerful tools for extracting data from images and videos
The Python programming language is an ideal platform for rapidly prototyping and developing production-grade codes for image processing and computer vision with its robust syntax and wealth of powerful libraries.
This video course will start by showing you how to set up Anaconda Python for the major OSes with cutting-edge third-party libraries for computer vision. You’ll learn state-of-the-art techniques to classify images and find and identify humans within videos.
Next, you’ll understand how to set up Anaconda Python 3 for the major OSes (Windows, Mac, and Linux) and augment it with the powerful vision and machine learning tools OpenCV and TensorFlow, as well as Dlib. You’ll be taken through the handwritten digits classifier and then move on to detecting facial features and finally develop a general image classifier.
By the end of this course, you’ll know the basic tools of computer vision and be able to put it into practice.
ISBN 13: 9781789532029 Packt 194 Pages (June 2018)
Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
ISBN 13: 9781789533972 Packt 138 Pages (June 2018)
Leverage the simplicity of Python and available libraries to build web security testing tools for your application
Learning Python Web Penetration Testing will walk you through the web application penetration testing methodology, showing you how to write your own tools with Python for each activity throughout the process. The book begins by emphasizing the importance of knowing how to write your own tools with Python for web application penetration testing.
You will then learn to interact with a web application using Python, understand the anatomy of an HTTP request, URL, headers and message body, and later create a script to perform a request, and interpret the response and its headers. As you make your way through the book, you will write a web crawler using Python and the Scrappy library. The book will also help you to develop a tool to perform brute force attacks in different parts of the web application.
ISBN 13: 9781788623315 Packt 280 Pages (June 2018)
Design, simulate, and program interactive robots
Robot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implementing different capabilities in a robot without implementing them from scratch.
This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you'll learn about robot modeling and how to design and simulate it using ROS. Moving on, we'll design robot hardware and interfacing actuators. Then, you'll learn to configure and program depth sensors and LIDARs using ROS. Finally, you'll create a GUI for your robot using the Qt framework.
By the end of this tutorial, you'll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package.
Greg L. Turnquist, Bhaskar N. Das
ISBN 13: 9781787122529 Packt 396 Pages (June 2018)
Fix everyday testing problems in Python with the help of this solution-based guide
Automated testing is the best way to increase efficiency while reducing the defects of software testing. It helps find bugs in code easily and at an early stage so that they can be tackled efficiently. This book delves into essential testing concepts used in Python to help you build robust and maintainable code.
Python Testing Cookbook begins with a brief introduction to Python's unit testing framework to help you write automated test cases. You will learn how to write suitable test sets for your software and run automated test suites with Nose. You will then work with the unittest.mock library, which allows you to replace the parts of your system that are being tested with mock objects and make assertions about how they have been used. You will also see how to apply Test-driven Development (TDD) and Behavior-driven Development (BDD) and how to eliminate issues caused by TDD. The book explains how to integrate automated tests using Continuous Integration and perform smoke/load testing. It also covers best practices and will help you solve persistent testing issues in Python. The book concludes by helping you understand how doctest works and how Selenium can be used to test code efficiently.
ISBN 13: 9781788999137 Packt Course Length: 2 hours 22 minutes (June 2018)
Build powerful Machine Learning models using Python with hands-on practical examples in just a week.
Machine learning is one of the most sought-after skills in the market. But have you ever wondered where to start or found the course not so easy to follow. With this hands-on and practical machine learning course, you can learn and start applying machine learning in less than a week without having to be an expert mathematician.
In this course, you will be introduced to a new machine learning aspect in each section followed by a practical assignment as a homework to help you in efficiently implement the learnings in a practical manner. With the systematic and fast-paced approach to this course, learn machine learning using Python in the most practical and structured way to develop machine learning projects in Python in a week.
This course is structured to unlock the potential of Python machine learning in the shortest amount of time. If you are looking to upgrade your machine learning skills using Python in the quickest possible time, then this course is for you!
ISBN 13: 9781788992855 Packt Course Length: 3 hours 34 minutes (June 2018)
Use Python to apply market basket analysis, PCA and dimensionality reduction, as well as cluster algorithms
This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code.
Say you have millions of transaction data on products purchased at a retailer. Which individual products or product categories are most likely to be purchased together? How about a large number of survey responses – which answers were most often given together, for all or some subset of respondents? Association Rules provide answers to these questions, and they are most frequently used in Market Basket Analysis. The Apriori Algorithms solves the formidable computational challenges of calculating Association Rules. After taking this course, you will be understanding and be able to apply the Apriori Algorithm to calculate, interpret and create interactive visualizations of association rules.
This course will allow you to utilize Principal Component Analysis, and to visualize and interpret the results of your datasets such as the ones in the above description. You will also be able to apply hard and soft clustering methods (k-Means and Gaussian Mixture Models) to assign segment labels to customers categorized in your sample data sets.
ISBN 13: 9781788995931 Packt Course Length: 2 hours 36 minutes (June 2018)
App development, data science, automation, web scraping or computer vision—Make the switch to Python quickly
You are a programmer. You already know many languages, but Python isn't one of them. Today, you need to get up-to-speed with Python in a short period of time, but your search has so far come up with disconnected, unrelated tutorials or guides.
Learn Python in 3 hours is a fast-paced, action-packed course that maximizes your time; it's designed from the ground up to bring you from zero to hero in the shortest time. The course is based on many years of Python development experience in both large enterprises and nimble startups. In particular, the course's hands-on and practical approach comes from the author's experience in rapidly iterating and shipping products in a startup setting, where responsiveness and speed are key.
With Learn Python in 3 hours, you will be up-and-running with Python like you are with your other languages, proving your value and expertise to your team today, and building your CV and skillset for tomorrow
Indra den Bakker
ISBN 13: 9781789531602 Packt Course Length: 1 hour 45 minutes (June 2018)
Over 20 practical videos on neural network modeling, reinforcement learning, and transfer learning using Python
Deep Learning is revolutionizing a wide range of industries. For many applications, Deep Learning has been proven to outperform humans by making faster and more accurate predictions. This course provides a top-down and bottom-up approach to demonstrating Deep Learning solutions to real-world problems in different areas.
These applications include Computer Vision, Generative Adversarial Networks, and time series. This course presents technical solutions to the issues presented, along with a detailed explanation of the solutions.
Furthermore, it provides a discussion on the corresponding pros and cons of implementing the proposed solution using a popular framework such as TensorFlow, PyTorch, and Keras. The course includes solutions that are related to the basic concepts of neural networks; all techniques, as well as classical network topologies, are covered. The main purpose of this video course is to provide Python programmers with a detailed list of solutions so they can apply Deep Learning to common and not-so-common scenarios.
ISBN 13: 9781789539523 Packt Course Length: 3 hours 10 minutes (June 2018)
Learn cutting-edge pattern matching skills for log mining, big data parsing, cleanup and preparation with Regex
Welcome to Python Regular Expressions Course! You will apply your new skills with four hands-on real-world projects. You will gain solid understanding on type of performance issues regex can run into, and techniques to address them. As part of resources in this course, you will get a high-quality quick reference guide, an interactive tool, all the source code and downloadable slides. Why Learn Regular Expressions? Very often, the data that we need is not readily accessible or useful.
Data preparation and clean-up is often one of the most time-consuming activities in a software automation project. Instead of spending time writing code for all this, you can specify data patterns of interest and let regular expression engine do the work for you. Regular Expression is cross-platform and you can learn the concepts once and use it in multiple programming languages and environment Looking forward to seeing you in the course! God Speed!
ISBN 13: 9781789135817 Packt Course Length: 2 hours 46 minutes (June 2018)
Transform your simple machine learning model into a cutting edge powerful version
Machine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. Python makes this easier with its huge set of libraries that can be easily used for machine learning. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python.
Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox.
By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.
ISBN 13: 9781788478311 Packt 472 Pages (May 2018)
Write modern natural language processing applications using deep learning algorithms and TensorFlow.
Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator.
After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
ISBN 13: 9781789340495 Packt Course Length: 1 hours 43 minutes (MAY 2018)
Utilize Python’s most efficient libraries—pandas, matplotlib, and Seaborn—for data visualization and time series analysis
Visualization is a critical component in exploratory data analysis, as well as presentations and applications. If you are struggling in your day-to-day data analysis tasks, then this is the right course for you. This fast-pace guide follows a recipe-based approach, each video focusing on a commonly-faced issue.
This course covers advanced and powerful time series capabilities so you can dissect by any possible dimension of time. It introduces the Matplotlib library, which is responsible for all of the plotting in pandas, at the same time focusing on the pandas plot method and the Seaborn library, which is capable of producing aesthetically pleasing visualizations not directly available in pandas. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.
Dr. Yuxing Yan, James Yan
ISBN 13: 9781788831192 Packt 364 Pages (May 2018)
Develop, deploy, and streamline your data science projects with the most popular end-to-end platform, Anaconda
Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world.
The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod.
ISBN 13: 9781788832922 Packt 340 Pages (May 2018)
Leverage the power of python to build DIY robotic projects
Robotics is a fast-growing industry. Multiple surveys state that investment in the field has increased tenfold in the last 6 years, and is set to become a $100-billion sector by 2020. Robots are prevalent throughout all industries, and they are all set to be a part of our domestic lives. This book starts with the installation and basic steps in configuring a robotic controller.
You'll then move on to setting up your environment to use Python with the robotic controller. You'll dive deep into building simple robotic projects, such as a pet-feeding robot, and more complicated projects, such as machine learning enabled home automation system (Jarvis), vision processing based robots and a self-driven robotic vehicle using Python.
ISBN 13: 9781788294874 Packt 380 Pages (MAY 2018)
Test your Python programming skills by solving real-world problems
This book covers the unexplored secrets of Python, delve into its depths, and uncover its mysteries. You’ll unearth secrets related to the implementation of the standard library, by looking at how modules actually work. You’ll understand the implementation of collections, decimals, and fraction modules. If you haven’t used decorators, coroutines, and generator functions much before, as you make your way through the recipes, you’ll learn what you’ve been missing out on.
We’ll cover internal special methods in detail, so you understand what they are and how they can be used to improve the engineering decisions you make. Next, you’ll explore the CPython interpreter, which is a treasure trove of secret hacks that not many programmers are aware of. We’ll take you through the depths of the PyPy project, where you’ll come across several exciting ways that you can improve speed and concurrency.
ISBN 13: 9781788621700 Packt Course Length: 1 hours 51 minutes (May 2018)
Learn how to apply the concepts of deep learning to a diverse range of natural language processing (NLP) techniques
In this course, you’ll expand your NLP knowledge and skills while implementing deep learning tools to perform complex tasks. You’ll start by preparing your environment for NLP and then quickly learn about language structure and how we can break sentences down to extract information and uncover the underlying meaning. After reviewing the basics, we’ll move on to speech recognition and show how deep learning can be used to build speech recognition applications.
By the end of this tutorial, you’ll have a better understanding of NLP and will have worked on multiple examples that implement deep learning to solve real-world spoken language problems. In particular, you’ll be able to discover useful information and extract key insights from piles of natural language data.
ISBN 13: 9781788629744 Packt Course Length: 2 hours 26 minutes (May 2018)
A practical approach to Object-Oriented Programming (OOP) in Python 3.
The world is filled with coders, who write pieces of programs in a bid to find solutions to various problems. In such a field where the competition is already intense, you need a definitive edge over the rest. One of the better ways to stay ahead of the pack is to write smarter code. Writing large programs can be painful. That’s where Object-Oriented Programming (OOP) comes to the rescue. OOP saves a considerable amount of coding man-hours in the long run by writing code in a smarter way, through various techniques.
To ensure efficient coding, you will be introduced to Exceptions and Error Handling. Furthermore, Encapsulation with methods and variables will help you to keep data safe from external, unwanted interference. In the final sections, you will be taken through recursion mechanisms.
By the end of this course, you will be well-versed with the OOP techniques in Python 3, which will help you to write codes better and in an efficient manner.
ISBN 13: 9781788471312 Packt Course Length: 1 hour 21 minutes (May 2018)
Learn how to model and visualize data in Python through Pandas data library
This video course will support users as they work through a typical real-world data analysis project step-by-step using Pandas. It develops the deep knowledge and skills that will enable students to immediately tackle their own projects with Pandas at work.
This product demonstrates how to make financial models using Python's software library for data manipulation and analysis.
ISBN 13: 9781788625807 Packt Course Length: 3 hours 20 minutes (May 2018)
Practical coding projects to get you up and running with Python in no time
This video course explores Python basics, data structures, and algorithms. We'll build a die rolling simulator to see how to use Python dictionaries, loops, functions, and control statements.
Next we will use a modular approach to build a game that consists of a deck of playing cards. We will use object-oriented (OOP) Python classes to do so. We will display the playing cards both in a textual form, which we create, as well as via image files. This will lead to our displaying card images in a graphical form using Python's built-in Tkinter package.
ISBN 13: 9781788295499 Packt Course Length: 1 hours 44 minutes (May 2018)
Turn practical hands-on projects such as language processing, computer vision, sentiment analysis, and text processing into useful application in Python to take your skills to another level!
You’ll learn to read text in documents using different models, and employ sentiment analysis to predict the author’s intent. You’ll also see how to employ Python to read images and for computer vision. Once you’ve learned to employ specific Python packages and syntax for these tasks, you’ll explore case studies that put forth solid real-world examples on spam filtering and analyzing human emotions through a dictionary of images.
This course contains in-depth content balanced with tutorials that put theory into practice. This course will give you both a theoretical understanding and practical exp with examples that will allow you indulge in the art of statistical modeling and analysis using the Python programming language.
ISBN 13: 9781789340525 Packt Course Length: 2 hours and 25 minutes (May 2018)
A practical introduction to the world of machine learning and image processing using OpenCV and Python
This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems.
The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems.
ISBN 13: 9781789138313 Packt Course Length: 2 hours and 18 minutes (May 2018)
Apply the practices of Test-Driven Development using the PyTest framework to easily create your unit tests
In this video tutorial, you’ll learn about the PyTest testing library and how it’s used to write unit tests in Python. You’ll also set up some common Python development environments to use PyTest. You’ll create isolated test environments with Test Doubles and learn how to implement and use them with unittest.mock. Moving on, you’ll get to know some of the best practices in Unit Testing and TDD and get some hands-on experience with programming by implementing unit tests using TDD in Python.
By the end of this course, you’ll be able to apply the practices of Unit Testing and TDD on a daily basis to radically increase the quality of your code and help you and your company achieve your goals faster than ever before.
Gastón C. Hillar
ISBN 13: 9781789138542 Packt 228 Pages (May 2018)
Explore the features included in the latest versions of MQTT for IoT and M2M communications and use them with modern Python 3.
MQTT is a lightweight messaging protocol for small sensors and mobile devices. This book explores the features of the latest versions of MQTT for IoT and M2M communications, how to use them with Python 3, and allow you to interact with sensors and actuators using Python.
The book begins with the specific vocabulary of MQTT and its working modes, followed by installing a Mosquitto MQTT broker. You will use different utilities and diagrams to understand the most important concepts related to MQTT. You will learn to make all the necessary configuration to work with digital certificates for encrypting all data sent between the MQTT clients and the server. You will also work with the different Quality of Service levels and later analyze and compare their overheads.
You will write Python 3.x code to control a vehicle with MQTT messages delivered through encrypted connections (TLS 1.2), and learn how leverage your knowledge of the MQTT protocol to build a solution based on requirements. Towards the end, you will write Python code to use the PubNub cloud-based real-time MQTT provider to monitor a surfing competition.
In the end, you will have a solution that was built from scratch by analyzing the requirements and then write Python code that will run on water-proof IoT boards connected to multiple sensors in surfboards.
Alan D. Moore
ISBN 13: 9781788835886 Packt 452 Pages (May 2018)
Find out how to create visually stunning and feature-rich applications by empowering Python's built-in TKinter GUI toolkit
Tkinter is a lightweight, portable, and easy-to-use graphical toolkit available in the Python Standard Library, widely used to build Python GUIs due to its simplicity and availability. This book teaches you to design and build graphical user interfaces that are functional, appealing, and user-friendly using the powerful combination of Python and Tkinter.
After being introduced to Tkinter, you will be guided step-by-step through the application development process. Over the course of the book, your application will evolve from a simple data-entry form to a complex data management and visualization tool while maintaining a clean and robust design. In addition to building the GUI, you'll learn how to connect to external databases and network resources, test your code to avoid errors, and maximize performance using asynchronous programming. You'll make the most of Tkinter's cross-platform availability by learning how to maintain compatibility, mimic platform-native look and feel, and build executables for deployment across popular computing platforms.
By the end of this book, you will have the skills and confidence to design and build powerful high-end GUI applications to solve real-world problems.
ISBN 13: 9781789138962 Packt 230 Pages (May 2018)
This book gives you the skills you need to use Python for penetration testing, with the help of detailed code examples. This book has been updated for Python 3.6.3 and Kali Linux 2018.1.
We start by exploring the basics of networking with Python and then proceed to network hacking. Then, you will delve into exploring Python libraries to perform various types of pentesting and ethical hacking techniques. Next, we delve into hacking the application layer, where we start by gathering information from a website. We then move on to concepts related to website hacking—such as parameter tampering, DDoS, XSS, and SQL injection.
By reading this book, you will learn different techniques and methodologies that will familiarize you with Python pentesting techniques, how to protect yourself, and how to create automated programs to find the admin console, SQL injection, and XSS attacks.
Krishna Bhavsar, Naresh Kumar, Pratap Dangeti
ISBN 13: 9781789348989 Packt Course Length: 1 hour 24 minutes (April 2018)
Learn the tricks and tips that will help you design Text Analytics solutions
Natural Language Processing (NLP) is a feature of Artificial Intelligence concerned with the interactions between computers and human (natural) languages. This course includes unique videos that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task.
In this course, you will learn what WordNet is and explore its features and usage. It will teach how to extract raw text from web sources and introduce some critical pre-processing steps. You will also get familiarized with the concept of pattern matching as a way to do text analysis.
By the end of the course, you will be confident & have covered various solutions, covering natural language understanding, Natural Language Processing, and syntactic analysis.
ISBN 13: 9781788475266 Packt Course Length: 4 hours 42 minutes (April 2018)
Build useful and effective deep learning models with the PyTorch Deep Learning framework
In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.
By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.
ISBN 13: 9781789137361 Packt Course Length: 2 hours 26 minutes (April 2018)
Get high-quality information from your text using Machine Learning with Tensorflow, NLTK, Scikit-Learn, and Python
Text is one of the most actively researched and widely spread types of data in the Data Science field today. New advances in machine learning and deep learning techniques now make it possible to build fantastic data products on text sources. New exciting text data sources pop up all the time. You'll build your own toolbox of know-how, packages, and working code snippets so you can perform your own text mining analyses.
You'll start by understanding the fundamentals of modern text mining and move on to some exciting processes involved in it. You'll learn how machine learning is used to extract meaningful information from text and the different processes involved in it. You will learn to read and process text features. Then you'll learn how to extract information from text and work on pre-trained models, while also delving into text classification, and entity extraction and classification. You will explore the process of word embedding by working on Skip-grams, CBOW, and X2Vec with some additional and important text mining processes. By the end of the course, you will have learned and understood the various aspects of text mining with ML and the important processes involved in it, and will have begun your journey as an effective text miner.
ISBN 13: 9781788999229 Packt Course Length: 3 hours 17 minutes (April 2018)
Practical and unique solutions to common Machine Learning problems that you face. Avoid any roadblocks while working with the Python data science ecosystem.
You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. What do you do?
Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge.
ISBN 13: 9781788838979 Packt 176 Pages (April 2018)
Your one-stop guide to using Python, creating your own hacking tools, and making the most out of resources available for this programming language
This book is packed with step-by-step instructions and working examples to make you a skilled penetration tester. It is divided into clear bite-sized chunks, so you can learn at your own pace and focus on the areas of most interest to you. This book will teach you how to code a reverse shell and build an anonymous shell. You will also learn how to hack passwords and perform a privilege escalation on Windows with practical examples. You will set up your own virtual hacking environment in VirtualBox, which will help you run multiple operating systems for your testing environment.
By the end of this book, you will have learned how to code your own scripts and mastered ethical hacking from scratch.
Krishna Bhavsar, Naresh Kumar, Pratap Dangeti
ISBN 13: 9781789343335 Packt Course Length: 1 hour and 17 minutes ( April 30, 2018 )
Learn a practical viewpoint to understand and implement NLP solutions involving POS tagging, parsing, and much more
This course is designed with advanced solutions that will take you from newbie to pro in performing Natural Language Processing with NLTK. In this course, you will come across various concepts covering natural language understanding, Natural Language Processing, and syntactic analysis.
It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis.By the end of this course, you will have all the knowledge you need to implement Natural Language Processing with Python.
Aldrin Yim, Claire Chung, Allen Yu
ISBN 13: 9781788625173 Packt 300 Pages (April 2018)
Leverage the power of Matplotlib to visualize and understand your data more effectively
Matplotlib is a popular data visualization package in Python used to design effective plots and graphs. This is a practical, hands-on resource to help you visualize data with Python using the Matplotlib library.
Matplotlib for Python Developers, Second Edition shows you how to create attractive graphs, charts, and plots using Matplotlib. You will also get a quick introduction to third-party packages, Seaborn, Pandas, Basemap, and Geopandas, and learn how to use them with Matplotlib. After that, you’ll embed and customize your plots in third-party tools such as GTK+3, Qt 5, and wxWidgets. You’ll also be able to tweak the look and feel of your visualization with the help of practical examples provided in this book.
Steven F. Lott
ISBN 13: 9781788627061 Packt 408 Pages (April 2018)
Create succinct and expressive implementations with functional programming in Python
Starting with a general overview of functional concepts, you’ll explore common functional features such as first-class and higher-order functions, pure functions, and more. You’ll see how these are accomplished in Python 3.6 to give you the core foundations you’ll build upon. After that, you’ll discover common functional optimizations for Python to help your apps reach even higher speeds.
You’ll learn FP concepts such as lazy evaluation using Python’s generator functions and expressions. Moving forward, you’ll learn to design and implement decorators to create composite functions. You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. Finally, to top off your journey into the world of functional Python, you’ll at look at the PyMonad project and some larger examples to put everything into perspective.
ISBN 13: 9781788390040 Packt 566 Pages (April 2018)
Practical, hands-on solutions in Python to overcome any problem in Machine Learning
The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions.
In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Paul Crickard, Eric van Rees, Silas Toms
ISBN 13: 9781788293334 Packt 440 Pages (April 2018)
Explore GIS processing and learn to work with various tools and libraries in Python.
Python comes with a host of open source libraries and tools that help you work on professional geoprocessing tasks without investing in expensive tools. This book will introduce Python developers, both new and experienced, to a variety of new code libraries that have been developed to perform geospatial analysis, statistical analysis, and data management. This book will use examples and code snippets that will help explain how Python 3 differs from Python 2, and how these new code libraries can be used to solve age-old problems in geospatial analysis.
You will begin by understanding what geoprocessing is and explore the tools and libraries that Python 3 offers. You will then learn to use Python code libraries to read and write geospatial data. You will then learn to perform geospatial queries within databases and learn PyQGIS to automate analysis within the QGIS mapping suite. Moving forward, you will explore the newly released ArcGIS API for Python and ArcGIS Online to perform geospatial analysis and create ArcGIS Online web maps. Further, you will deep dive into Python Geospatial web frameworks and learn to create a geospatial REST API.
James Lee et al
ISBN 13: 9781789133394 Packt Course Length: 3 hours and 18 minutes (April 2018)
Learn Apache Spark and Python by 12+ hands-on examples of analyzing big data with PySpark and Spark
This course covers all the fundamentals of Apache Spark with Python and teaches you everything you need to know about developing Spark applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adopt Apache Spark for building big data processing pipeline and data analytics applications.
This course covers 10+ hands-on big data examples. You will learn valuable knowledge about how to frame data analysis problems as Spark problems. Together we will learn examples such as aggregating NASA Apache weblogs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how makerspaces are distributed across different regions in the United Kingdom. And much much more.
Jalem Raj Rohit
ISBN 13: 9781787288676 Packt 272 Pages (April 2018)
Building efficient Python applications at minimal cost by adopting serverless architectures
Serverless architectures allow you to build and run applications and services without having to manage the infrastructure. Many companies have adopted this architecture to save cost and improve scalability. This book will help you design serverless architectures for your applications with AWS and Python.
The book is divided into three modules. The first module explains the fundamentals of serverless architecture and how AWS lambda functions work. In the next module, you will learn to build, release, and deploy your application to production. You will also learn to log and test your application. In the third module, we will take you through advanced topics such as building a serverless API for your application. You will also learn to troubleshoot and monitor your app and master AWS lambda programming concepts with API references. Moving on, you will also learn how to scale up serverless applications and handle distributed serverless systems in production.
Aubrey Love II
ISBN 13: 9781788835206 Packt Course Length: 2 hours 45 minutes (APRIL 2018)
Five languages in five hours. Get started with programming in HTML5, CSS3, Python, C++, and more!
In this course, you will get to know 5 different languages, compilers, and editors for programming and then you will use HTML5 and CSS3 to build your own company website. As you have successfully stepped into the world of programming, you will now delve into one of the most commonly used Android systems and build an amazing Android application for your company. Next you will master Python and start scripting to build your own Hello World Program. For many people, C++ is just a dream but you will easily master this language and program your Hello World App with C++. Now since you know 5 top languages, you can create a good résumé, create online visibility, and forge ahead in your career. You'll understand several aspects of application development and we guarantee that, by the end of the video, you'll have your very own application up-and-running. For the brave at heart, we also have exercises, at the end of each chapter, which will intrigue you and encourage you to add your own personal touch to the code (and ultimately your application).
ISBN 13: 9781788830416 Packt Course Length: 1 hour 06 minutes (APRIL 2018)
Data Visualization with bqplot, NetworkX, and Bokeh in Python
You will start by programming stunning interactive Data Visualizations using bqplot, an open source Python library developed by Bloomberg. Then you will learn how to programmatically create interactive network graphs and visualizations. You will then programmatically visualize data with the interactive Python visualization library, Bokeh.
Finally, you will build interactive web visualizations of data using Python: you will choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs.
ISBN 13: 9781788621953 Packt Course Length: 14 hours 22 minutes (APRIL 2018)
Make your day and master the Python: a powerful and versatile language and ecosystem that can help you save time and money. This video course is about leveraging the Python programming language and its thriving ecosystem to save yourself time and money when doing common routine tasks. Nobody wants to do boring and time-consuming tasks: days have 24 hours and you should squeeze out the most of this time for yourself - automating the boring tasks gives you back time to focus on what you really like to do. Moreover, this is also the chance for you to learn a great general-purpose language such as Python, with which you can build very cool applications both at work and in your spare time.
The course is structured as an incremental learning path: you will start with a deep-dive into Python software development basics, then move on to write scripts to automate file system operations and file contents processing on your local host, then you will learn how to interact with web-based services such as websites and APIs in order to robotize the cool things that we do everyday - such as tweeting, posting to social networks, reading RSS feeds, etc. - moreover you will practice how to set up a web-based services yourself in the form of web applications and in the end you will learn how to analyze and visualize datasets in order to extract knowledge. By the end of this course you will have learned how to proficiently write structured Python code in a wide range of applications – from one-liner scripts to complex web applications – aiming at the automation of lots of common everyday life tasks.
ISBN 13: 9781788629928 Packt Course Length: 3 hours 58 minutes (MARCH 2018)
Learn everything you need to know about Machine learning with Tensorflow and Scikit-Learn
Machine Learning is one of the most transformative and impactful technologies of our time. From advertising to healthcare, to self-driving cars, it is hard to find an industry that has not been or is not being revolutionized by machine learning. Using the two most popular frameworks, Tensor Flow and Scikit-Learn, this course will show you insightful tools and techniques for building intelligent systems. Using Scikit-learn you will create a Machine Learning project from scratch, and, use the Tensor Flow library to build and train professional neural networks.
We will use these frameworks to build a variety of applications for problems such as ad ranking and sentiment classification. The course will then take you through the methods for unsupervised learning and what to do when you have limited or no labels for your data. We use the techniques we have learned, along with some new ones, to build a sentiment classifier, an autocomplete keyboard and a topic discoverer.
ISBN 13: 9781788392402 Packt Course Length: 4 hours 28 minutes (MARCH 2018)
A practical tour of prediction and control in Reinforcement Learning using OpenAI Gym, Python, and TensorFlow
Reinforcement learning (RL) is hot! This branch of machine learning powers AlphaGo and Deepmind's Atari AI. It allows programmers to create software agents that learn to take optimal actions to maximize reward, through trying out different strategies in a given environment.
This course will take you through all the core concepts in Reinforcement Learning, transforming a theoretical subject into tangible Python coding exercises with the help of OpenAI Gym. The videos will first guide you through the gym environment, solving the CartPole-v0 toy robotics problem, before moving on to coding up and solving a multi-armed bandit problem in Python. As the course ramps up, it shows you how to use dynamic programming and TensorFlow-based neural networks to solve GridWorld, another OpenAI Gym challenge. Lastly, we take the Blackjack challenge and deploy model free algorithms that leverage Monte Carlo methods and Temporal Difference (TD, more specifically SARSA) techniques.
ISBN 13: 9781789139938 Packt Course Length: 1 hour 56 minutes (MARCH 2018)
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.
ISBN 13: 9781788995580 Course Length: 2 hours 14 minutes (March 2018)
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.
ISBN 13: 9781788832403 Packt Course Length: 2 hours 02 minutes (March 2018)
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.
ISBN 13: 9781789130157 Packt Course Length: 1 hour and 24 minutes (March 2018)
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.
ISBN 13: 9781789135091 Packt Course Length: 1 hour and 36 minutes (March 2018)
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.
Alexey Spizhevoy, Aleksandr Rybnikov
ISBN 13: 9781788474443 Packt 306 Pages (March 2018)
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.
ISBN 13: 9781789134346 Packt Course Length: 3 hours and 35 minutes (March 2018)
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.
ISBN 13: 9781788991056 Packt Course Length: 2 hours 39 minutes (March 2018)
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.
ISBN 13: 9781788297684 Packt 234 Pages (February 2018)
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.
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.
Harish Kumar Garg
ISBN 13: 9781788838658 Packt Course Length: 1 hours 17 minutes (February 2018)
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.
ISBN 13: 9781787285217 Packt 364 Pages (February 2018)
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.
ISBN 13: 9781788839068 Course Length: 2 hours 41 minutes (February 2018)
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.
ISBN 13: 9781788396592 Course Length: 2 hours 29 minutes (February 2018)
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.
BignumWorks Software LLP
ISBN 13: 9781788998031 Course Length: 2 hours 20 minutes (February 2018)
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.
BignumWorks Software LLP
ISBN 13: 9781788998031 Course Length: 2 hours 20 minutes (February 2018)
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.
ISBN 13: 9781788624336 Packt 262 Pages (February 2018)
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.
ISBN 13: 9781788479387 Course Length: 2 hours and 3 minutes (February 2018)
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.
Daniel Furtado, Marcus Pennington
ISBN 13: 9781786468161 Packt 456 Pages (February 2018)
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.
ISBN 13: 9781788394291 Packt Course Length: 3 hours 30 minutes (January 2018)
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.
Wei Di, Anurag Bhardwaj, Jianing Wei
ISBN 13: 9781785880360 Packt 284 Pages (Jan 2018)
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
ISBN 13: 9781788293402 Packt Course Length: 4 hours 5 minutes (Jan 2018)
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.
ISBN 13: 9781789132793 Packt Course Length: 1 hour 32 minutes (Jan 2018)
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.
ISBN 13: 9781785888632 Packt 548 Pages (Jan 2018)
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.
ISBN 13: 9781788297752 Packt 438 Pages (December 2017)
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.
ISBN 13: 9781788997522 Packt Course Length: 1 hour 43 minutes (December 2017)
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.
ISBN 13: 9781788991957 Packt Course Length: 4 hours 45 minutes (December 2017)
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.
ISBN 13: 9781788994989 Packt Course Length: 2 hours 10 minutes (December 2017)
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.
ISBN 13: 9781787286085 Packt Course Length: 1 hour 47 minutes (December 2017)
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.
Ashish Kumar, Joseph Babcock
ISBN 13: 9781788992367 Packt 660 Pages (December 2017)
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.
Dr. Avishek Pal, Dr. PKS Prakash
ISBN 13: 9781788995719 Packt Course Length: 2 hours 25 minutes (December 2017)
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.
L. Felipe Martins, Ruben Oliva Ramos, V Kishore Ayyadevara
ISBN 13: 9781788291460 Packt 386 Pages (December 2017)
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.
ISBN 13: 9781788397599 Packt Course Length: 1 hours 45 minutes (July 2017)
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.
Raúl Garreta, Guillermo Moncecchi, Trent Hauck, Gavin Hackeling
ISBN 13: 9781788833479 Packt 530 Pages (November 2017)
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.
Krishna Bhavsar, Naresh Kumar, Pratap Dangeti
ISBN 13: 9781787289321 Packt 294 Pages (November 2017)
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.
ISBN 13: 9781788295321 Packt Course Length: 3 hours 44 minutes (November 2017)
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.
ISBN 13: 9781788397179 Packt Course Length: 1 hours 30 minutes (November 2017)
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.
ISBN: 978-1-387-37932-3 – 300 pages – (December 2017)
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.
ISBN 13: 9781787281998 Packt Publishing Course Length: 2 hours 56 minutes (October 2017)
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.
ISBN 13: 9781788471268 Packt Publishing Course Length: 4 hours 09 minutes (October 2017)
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.
ISBN 13: 9781786460677 Packt Publishing Course Length: 2 hours 26 minutes (September 2017)
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.
ISBN 13: 9781787280083 Packt Publishing Course Length: 1 hour 17 minutes (September 2017)
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.
Anand Balachandran Pillai
ISBN 13: 9781788838276 Packt Publishing Course Length: 1 hour 37 minutes (September 2017)
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.
ISBN 13: 9781788838207 Packt Publishing Course Length: 1 hour 29 minutes (September 2017)
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.
ISBN 13: 9781788291415 Packt Publishing Course Length: 2 hours 39 minutes (September 2017)
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.
ISBN 13: 9781788626255 Packt Publishing Course Length: 3 hours 16 minutes (September 2017)
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.
ISBN 13: 9781788628068 Packt Publishing Course Length: 5 hours 57 minutes (September 2017)
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.
Sebastian Raschka, Vahid Mirjalili
ISBN 13: 9781787125933 Packt Publishing 622 pages (September 2017)
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.
ISBN 13: 9781788294294 Packt Publishing Course Length: 2 hours 32 minutes (August 2017)
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.
ISBN 13: 9781788297448 Packt Publishing Course Length: 4 hours 10 minutes (August 2017)
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.
Pradeeban Kathiravelu, Dr. M. O. Faruque Sarker
ISBN 13: 9781786463999 Packt Publishing 450 pages (August 2017)
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.
ISBN 13: 9781788292450 Packt Publishing Course Length: 2 hours and 34 minutes (July 2017)
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
ISBN 13: 9781788299879 Packt Publishing 254 pages (July 2017)
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
ISBN 13: 9781787121423 Packt Publishing 486 pages (July 2017)
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
ISBN 13: 9781787129313 Packt Publishing 374 pages (July 2017)
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
Siddhartha Chatterjee, Michal Krystyanczuk
ISBN 13: 9781787121485 Packt Publishing 312 pages (July 2017)
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
ISBN 13: 9781785881114 Packt Publishing 340 pages (July 2017)
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
ISBN 13: 9781784397005 Packt Publishing 446 pages (June 2017)
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
Prabhanjan Tattar, Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta
ISBN 13: 9781787129627 Packt Publishing 434 pages (June 2017)
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
ISBN 13: 9781787121195 Packt Publishing Course Length: 2 hours and 18 minutes (June 2017)
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
ISBN 13: 9781787125698 Packt Publishing 586 pages (June 2017)
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
Katharine Jarmul, Richard Lawson
ISBN 13: 9781786462589 Packt Publishing 220 pages (May 2017)
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 fil