The goal of this book was to introduce you to the world of machine learning and prepare you to become a machine learning practitioner. Now that you know everything about the fundamental algorithms, you might want to investigate some topics in more depth.
Although it is not necessary to understand all of the details of all of the algorithms we implemented in this book, knowing some of the theory behind them might just make you a better data scientist.
If you are looking for more advanced material, then you might want to consider some of the following classics:
- Stephen Marsland, Machine Learning: An Algorithmic Perspective, Second Edition, Chapman and Hall/Crc, ISBN 978-146658328-3, 2014
- Christopher M. Bishop, Pattern Recognition and Machine Learning. Springer, ISBN 978-038731073-2, 2007
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of...
