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From the Publisher

What’s the key takeaway from your book?

That machine learning can be useful in almost every problem domain. I cover a lot of different subfields of machine learning in my book; by providing hands-on examples for each one of those topics, 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 broad audience of experienced programmers, as well as people who are new to programming. And by introducing the basic mathematics behind machine learning, we can appreciate machine learning being more than just black box algorithms, giving readers an intuition of the capabilities but also limitations of machine learning, and how to apply those algorithms wisely.

What’s new & updated in this second edition of Python Machine Learning?

Oh, where should I start. As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. Don’t worry – we didn’t remove it – but it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its release by Google in November 2015.

Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focusing on deep learning applications. In a similar vein to the rest of the book, these new chapters not only provide readers with practical instructions and examples, but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works.

What makes this book stand out from other machine learning titles?

I certainly can’t speak about all books on the market. However, since the first edition was released, I engaged in countless discussions with my readers, to help them with particular questions and to get their opinion on the parts they found unclear or topics they wish I had covered.

The connection between theory and praxis in particular was what readers found most helpful and somewhat lacking from other introductory texts (which, I heard, were either too theoretical or too practical). This constructive feedback has been invaluable for the second edition, helping me to focus on those parts that were still left unclear.

In a nutshell, the second edition of Python Machine Learning provides a healthy mix of theory and practical examples that most people found so helpful in the first edition, and the second edition adds on top of it with many refinements and additional topics based on the large corpus of invaluable reader feedback.

ASIN ‏ : ‎ 1787125939
Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (September 15, 2017)
Language ‏ : ‎ English
Paperback ‏ : ‎ 622 pages
ISBN-10 ‏ : ‎ 9781787125933
ISBN-13 ‏ : ‎ 978-1787125933
Item Weight ‏ : ‎ 2.35 pounds
Dimensions ‏ : ‎ 9.25 x 7.5 x 1.28 inches

Python Machine Learning – Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Are you looking to dive deep into the world of machine learning and deep learning using Python? Look no further than the second edition of Python Machine Learning.

In this comprehensive guide, you will learn how to implement various machine learning algorithms using scikit-learn and TensorFlow. From basic concepts to advanced techniques, this book covers it all.

Whether you are a beginner or an experienced data scientist, this book will provide you with the knowledge and skills needed to tackle real-world machine learning problems.

Get your hands on a copy of Python Machine Learning – Second Edition and start building intelligent systems today!
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