12 best books on pythons

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More about this book

So, naturally you are excited about Machine Learning and would love to join the party! Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or teach it to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and you could likely unearth some hidden gems if you just knew where to look. With Machine Learning, you could accomplish the following:

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What's new in this third edition? 

Many readers have told us how much they love the first 12 chapters of the book as a comprehensive introduction to machine learning and Python's scientific computing stack. To keep these chapters relevant and to improve the explanations based on reader feedback, we updated them to support the latest versions of NumPy, SciPy, and scikit-learn.

One of the most exciting events in the deep learning world was the release of TensorFlow 2. Consequently, all the TensorFlow-related deep learning chapters have received a big overhaul. Since TensorFlow 2 introduced many new features and fundamental changes, we rewrote these chapters from scratch. Furthermore, we added a new chapter on Generative Adversarial Networks, which are one of the hottest topics in deep learning research, as well as a comprehensive introduction to reinforcement learning based on numerous requests from readers.

What are the key takeaways from your book?

Machine learning can be useful in almost every problem domain. We cover a lot of different subfields of machine learning in the book. My hope is that people can find inspiration for applying these fundamental techniques to drive their research or industrial applications. Also, using well-developed and maintained open source software makes machine learning very accessible to a wide audience of experienced programmers, as well as those who are new to programming.

Python Machine Learning Third Edition is also different from a>emphasis on practical code examples. However, I think this approach is highly valuable for both students and young researchers who are getting started in machine learning and deep learning. We heard from readers of previous editions that the book strikes a good balance between explaining the broader concepts supported with great hands-on examples, giving a light introduction to the mathematical underpinnings.

Why is it important to learn about GANs and reinforcement learning? 

The first GANs paper had just come out two years before we started working on the second edition, but we weren't sure of its relevance. However, GANs have evolved into one of the hottest and most widely used deep learning techniques. People use them for creating artwork, colorizing and improving the quality of photos, and to recreate old video game textures in higher resolutions. It goes without saying that an introduction to GANs was long overdue.

Another important machine learning topic not included in previous editions is reinforcement learning, which has received a massive boost in attention recently. Thanks to impressive projects such as DeepMind's AlphaGo and AlphaGo Zero, reinforcement learning has received extensive news coverage. And just recently, it’s been used to compete with the world's top e-sports players in the real-time strategy video game StarCraft II. We hope that our new chapters can provide an accessible and practical introduction to this exciting field.