Machine Learning Books you should read in 2020

Machine Learning Books you should read in 2020

What are the best Machine Learning books right now

Machine Learning became one of the hottest domain of Computer Science. Each larger company is either applying Machine Learning or thinking about doing so soon to solve their problems and understand their data sets. That means it’s time to learn about Machine Learning, especially if you’re looking for new Computer Science challenges. A great way to do that is to read a couple of books.

Introductory level

If you’re just getting started with Machine Learning definitely read this book:

Introduction to Machine Learning with Python is a gentle introduction into machine learning. It doesn’t assume any knowledge about Python and it introduces fundamental concepts and applications of machine learning, discussing various methods through examples. That’s the best book I’ve ever seen for an entry level Machine Learning Engineer.

Intermediate Level

If you’ve completed a bunch of machine learning projects for yourself and get accustomed to working with machine learning models, here are the books which will take you further:

Python Machine Learning is just a great practical book with a lot of actual examples of code. It starts gently and then proceeds to most recent advance in machine learning and deep learning. It’s very easy to read and will appeal to people at any level as the second edition even goes to cover GANs.

Hands-On Machine Learning with Scikit-Learn and TensorFlow is an amazing reference at mid-level. It covers all fundamentals (classification methods, dimensionality reduction) and then gets into neural networks and deep learning.

Pattern Recognition and Machine Learning goes through all basic algorithms starting with a good statistics revision. It’s mostly concerned on theoretical aspects of Machine Learning and is great as a companion to other, more practical books.

Expert

At expert level reading scientific papers often is much better than reading books, because knowledge is being updated as we speak. Machine Learning is really living its moment. However it is also great to have a bunch of book references in hand to go into deep learning fully:

Deep Learning with Python was written by a creator of Keras, one of the most popular machine learning libraries in Python. The book starts gently, is very practical, gives pieces of code you can use right away and has in general many useful tips on using deep learning. An absolute must read in deep learning.

Deep Learning is an amazing reference for deep learning algorithms. It doesn’t contain much code, but has great insights about how one should approach problems with machine learning: written by pioneers of deep learning. It covers virtually all currently used techniques.

If you’re more mathematically-oriented, then you’ll love Machine Learning: a Probabilistic Perspective . It’s a tour-de-force through mathematics behind all machine learning methods. You probably won’t be able to read it at once, but it’s very useful as a reference in machine learning research.