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Mastering Python 2E

You're reading from   Mastering Python 2E Write powerful and efficient code using the full range of Python's capabilities

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Product type Paperback
Published in May 2022
Last Updated in May 2022
Publisher Packt
ISBN-13 9781800207721
Length 710 pages
Edition 2nd Edition
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Author (1):
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Rick Hattem Rick Hattem
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Rick Hattem
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Table of Contents (21) Chapters Close

Preface 1. Getting Started – One Environment per Project FREE CHAPTER 2. Interactive Python Interpreters 3. Pythonic Syntax and Common Pitfalls 4. Pythonic Design Patterns 5. Functional Programming – Readability Versus Brevity 6. Decorators – Enabling Code Reuse by Decorating 7. Generators and Coroutines – Infinity, One Step at a Time 8. Metaclasses – Making Classes (Not Instances) Smarter 9. Documentation – How to Use Sphinx and reStructuredText 10. Testing and Logging – Preparing for Bugs 11. Debugging – Solving the Bugs 12. Performance – Tracking and Reducing Your Memory and CPU Usage 13. asyncio – Multithreading without Threads 14. Multiprocessing – When a Single CPU Core Is Not Enough 15. Scientific Python and Plotting 16. Artificial Intelligence 17. Extensions in C/C++, System Calls, and C/C++ Libraries 18. Packaging – Creating Your Own Libraries or Applications 19. Other Books You May Enjoy
20. Index

Machine learning

Machine learning is the branch of artificial intelligence that can learn by itself. This can be fully autonomous learning, learning based on pre-labeled data, or a combination of these.

We need a little bit of background information before we can dive into the libraries and the examples for this subject. Feel free to gloss over this section and jump straight to the libraries if you are already familiar with the types of machine learning.

Types of machine learning

As we have briefly covered in the introduction, machine learning roughly splits up into three different methodologies, but often uses a combination of several. To recap, we have the following three major branches:

  • Supervised learning
  • Reinforcement learning
  • Unsupervised learning

Naturally, there are many combinations of these, so we will discuss a few important distinct types of learning that are based on the branches above. The names themselves should already give...

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