What this book covers
Chapter 1, Python Fundamentals – Math, Strings, Conditionals, and Loops, explains how to code basic Python concepts, and outlines the fundamentals of the Python language.
Chapter 2, Python Data Structures, covers the essential elements that are used to store and retrieve data using general Python.
Chapter 3, Executing Python – Programs, Algorithms, and Functions, explains how to write more powerful and concise code through an increased appreciation of well-written algorithms, and an understanding of functions.
Chapter 4, Extending Python, Files, Errors, and Graphs, covers the basic I/O (input/output) operations for Python and covers using the matplotlib and seaborn libraries to create visualizations.
Chapter 5, Constructing Python – Classes and Methods, introduces one of the most central concepts in object-oriented programming classes, and it will help you write code using classes, which will make your life easier.
Chapter 6, The Standard Library, explains the importance of the Python standard library. It explains how to navigate in the standard Python libraries and overviews some of the most commonly used modules.
Chapter 7, Becoming Pythonic, covers the Python programming language, with which you will enjoy writing succinct, meaningful code. It also demonstrates some techniques for expressing yourself in ways that are familiar to other Python programmers.
Chapter 8, Software Development, shows how to debug and troubleshoot our applications, how to write tests to validate our code, and the documentation for other developers and users.
Chapter 9, Practical Python – Advanced Topics, explains how to take advantage of parallel programming, how to parse command-line arguments, how to encode and decode Unicode, and how to profile Python to discover and fix performance problems.
Chapter 10, Data Analytics with pandas and NumPy, introduces data science, which is a core application of Python. Loading, graphing, analyzing, and manipulating big data are all covered.
Chapter 11, Machine Learning, explains the concept of machine learning along with the necessary steps in building, scoring, and making predictions from a wide range of machine learning algorithms.
Chapter 12, Deep Learning with Python, explains the fundamental ideas and code behind neural networks, using Keras. Regularization techniques, including Dropout, and a full section on convolutional neural networks are included.
Chapter 13, New Features in Python, focuses on explaining the new features available in Python versions, from 3.7 to 3.11. It lists the enhancements in each version, with code samples on how to use them and why they are beneficial to the user, helping you to keep up to date with the evolution of the language.
