Book Image

Mastering Python 2E - Second Edition

By : Rick van Hattem
5 (1)
Book Image

Mastering Python 2E - Second Edition

5 (1)
By: Rick van Hattem

Overview of this book

Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python’s capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code’s performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges
Table of Contents (21 chapters)
19
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20
Index

Artificial Intelligence

In the last chapter, we saw a collection of scientific Python libraries that allow for really fast and easy processing of large data files. In this chapter, we will use some of these and a few others for machine learning.

Machine learning is a complex subject, and many completely distinct subjects within it are entire branches of research by themselves. This should not discourage you from diving in, however; many of the libraries mentioned in this chapter are really powerful and allow you to get started with a very reasonable amount of effort.

It should be noted that there is a huge difference between applying a pre-trained model and generating your own. Applying a model is usually possible in a few lines of code and barely requires any processing power; building your own model usually takes many lines of code and hours or more to process. This makes the training of models outside of the scope of this book in all but the most trivial cases. In these...