Best books to build Machine Learning understanding.
A well-written and thorough book can be an amazing path to build deeper understanding and also act as a handbook as you discover the internet's vast resources.
These are our and our experts top picks to get started building career-relevant skills.
Reinforcement Learning: An Introduction
Richard Sutton's book on Machine Learning is universally regarded as one of the most fundamental and important pieces on the matter. Reinforcement Learning is quickly becoming a major part of AI innovation, and a good read for any engineer and scientists to go through.
Generative Deep Learning
It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples o
Pattern Recognition and Machine Learning
Bishop's book on pattern recognition is a classic textbook and staple in Machine Learning. Beimg aimed at grad students, but also at researchers and practitioners, it's no easy lecture, but a truly fundamental course book.
Pragmatic Programmer
At least today, code is our door to building algorithms and complex Machine Learning systems. If you want to invest in becoming a more proficient Machine Learning professional faster, investing in code skills is the way to do so.
Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow
The hands-on Machine Learning book is an amazing piece by Aurélien Géron, taking you from the basics of Machine Learning to applying them to real-word scenarios all in one book.
Introduction to Statistical learning
ISL is a fundamental book and popular amongst undergrad and grad students for its clarity and simplicity with explaining concepts. The math required to understand the book is kept to a minimum, making it unique in its format.
Federated Learning (Synthesis Lectures)
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security.
