Machine Learning, 2nd Edition

APPENDIX DFurther Reading

Machine learning is only part of the story; it's the application of knowing what to use to get the insight you need. The domain of data science combines several disciplines that cover programming, math, domain knowledge, and visualization.

It's rare for one book to cover it all. To that end, I've included some further reading that will be of help to you on your machine learning and data journey. (I know what you're thinking, and yes, I have bought and read all of these books.)

Machine Learning

The machine learning arena is a huge domain and the majority of the books written are big, in-depth, heavy affairs that can take time to read, digest, and appreciate. Two stand out:

• Data Mining – Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, and Mark A. Hall (Morgan Kaufmann, 2011, ISBN 9780123748560)
• Collective Intelligence in Action by Satnam Alag (Manning, 2008, ISBN 9781933988313)

Statistics

More and more emphasis is being put on statistical knowledge and its application. Sometimes it feels hard to get into, especially for software developers, so these titles will help you along:

• The Art of Statistics by David Spielgalhalter (Pelican Books, 2019, ISBN 9780241398630)
• Naked Statistics: Stripping the Dread from the Data by Charles Wheelan (Norton, 2013, ISBN 9780393071955)
• Keeping Up with the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport and Jinho Kim (Harvard Business Review Press, 2013, ...