Top 3 Books to Kickstart Your Machine Learning Journey

Original article was published by Dario Radečić on Deep Learning on Medium

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

If you’re the type of person willing to spend months going through 800+ page book — congrats, you’re in for a treat.

This book is a long-time best-seller on Amazon, just because it covers everything one might need to work in the field, explained with perfect clarity. Seriously, the book covers topics from the machine learning definition to GANs and reinforcement learning.

I’m glad this book was mandatory on my machine learning college course, as I’ve learned much more from it than from the professor. If your professor isn’t Andrew Ng, but more someone like Siraj, I reckon it will do the same.

As mentioned earlier, the books goes from simple topics, like data gathering, EDA, feature scaling, to actual machine learning through algorithms such as decision trees, random forest, and gradient boosting. It also covers the main dimensionality reduction techniques and unsupervised learning. All of that in the first 300 pages!

The rest is reserved for neural networks and deep learning, from theory to application in the TensorFlow library. Expect to learn a lot about ANNs, CNNs, RNNs, Autoencoders, GANs, and reinforcement learning.

Another no brainer if you have the time. And will.

You can get the book here.