||Cracking Codes with Python|
Learn how to program in Python while making and breaking ciphers - algorithms used to create and send secret messages!
After a crash course in Python programming basics, you'll learn to make, test, and hack programs that encrypt text with classical ciphers like the transposition cipher and Vigenère cipher. You'll begin with simple programs for the reverse and Caesar ciphers and then work your way up to public key cryptography, the type of encryption used to secure today's online transactions, including digital signatures, email, and Bitcoin.
Each program includes the full code and a line-by-line explanation of how things work. By the end of the book, you'll have learned how to code in Python and you'll have the clever programs to prove it!
You'll also learn how to: Combine loops, variables, and flow control statements into real working programs; Use dictionary files to instantly detect whether decrypted messages are valid English or gibberish; Create test ...
||Applied Text Analysis with Python|
The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python's scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural language processing how to become more proficient in these two exciting areas of data science.
This book presents a concise, focused, and applied approach to text analysis with Python and covers topics including text ingestion and wrangling, basic machine learning on text, classification for text analysis, entity resolution, and text visualization. Applied Text Analysis with Python will enable you to design and develop language-aware data products.
You'll learn how and why machine learning algorithms make decisions about language to analyze text; how to ingest, wrangle, and p ...
||Complex Network Analysis in Python|
Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience.
Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive—such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study ...
||Mastering Machine Learning with Python in Six Steps|
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner.
This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.
You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining / analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation.
All the code presented in the book will be avail ...
||Practical Machine Learning with Python|
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.
Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.
Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and framewo ...
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You'll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process.
Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov's Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You'll then learn about Swarm Intelligence with Python in terms of reinforcement learning.
The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There's also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google's Deep Mind and see scenarios whe ...
||Test-Driven Development with Python, 2nd Edition|
By taking you through the development of a real web application from beginning to end, the second edition of this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python. You'll learn how to write and run tests before building each part of your app, and then develop the minimum amount of code required to pass those tests. The result? Clean code that works.
In the process, you'll learn the basics of Django, Selenium, Git, jQuery, and Mock, along with current web development techniques. If you're ready to take your Python skills to the next level, this book—updated for Python 3.6—clearly demonstrates how TDD encourages simple designs and inspires confidence.
Dive into the TDD workflow, including the unit test/code cycle and refactoring; Use unit tests for classes and functions, and functional tests for user interactions within the browser; Learn when and how to use mock objects, and the pros and cons of isolated vs. integr ...
Welcome to Scientific Python and its community. If you're a scientist who programs with Python this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You'll learn how to write elegant code that's clear, concise, and efficient at executing the task at hand.
Throughout the book, you'll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you'll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries.
Explore the NumPy array, the data structure that underlies numerical scientific computation; Use quantile normalization to ensure that measurements fit a specific distribution; Represent separate regions in an image with a Region Adjacency Graph; Convert temporal or spatial data into f ...
||Python Testing with pytest|
For Python-based projects, pytest is the undeniable choice to test your code if you're looking for a full-featured, API-independent, flexible, and extensible testing framework. With a full-bodied fixture model that is unmatched in any other tool, the pytest framework gives you powerful features such as assert rewriting and plug-in capability—with no boilerplate code.
With simple step-by-step instructions and sample code, this book gets you up to speed quickly on this easy-to-learn and robust tool. Write short, maintainable tests that elegantly express what you're testing. Add powerful testing features and still speed up test times by distributing tests across multiple processors and running tests in parallel. Use the built-in assert statements to reduce false test failures by separating setup and test failures. Test error conditions and corner cases with expected exception testing, and use one test to run many test cases with parameterized testing. Extend pytest with plugins, conn ...
||Practical Programming, 3rd Edition|
No programming experience required! Incremental examples show you the steps and missteps that happen while developing programs, so you know what to expect when you tackle a problem on your own. Inspired by “How to Design Programs” (HtDP), discover a five-step recipe for designing functions, which helps you learn the concepts—and becomes an integral part of writing programs.
In this detailed introduction to Python and to computer programming, find out exactly what happens when your programs are executed. Work with numbers, text, big data sets, and files using real-world examples. Create and use your own data types. Make your programs reliable, work with databases, download data from the web automatically, and build user interfaces. As you use the fundamental programming tools in this book, you'll see how to document and organize your code so that you and other programmers can more easily read and understand it. This new edition takes advantage of Python 3.6's new ...
||Foundations for Analytics with Python|
If you're like many of Excel's 750 million users, you want to do more with your data - like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats - by using Python.
After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.Create and run your own Python scripts by learning basic syntax;Use Python's csv module to read and parse CSV files;Read multiple Excel worksheets and workbooks with the xlrd module;Perform database operations in MySQL or with the mysqlclient module;Create Python ...