Real-World Machine Learning: Henrik Brink, Joseph Richards, Mark Fetherolf: 9781617291920: Amazon.com: Books

It is, however, a thoughtful introduction to and overview of machine-learning methods, appropriately remembering about the context and life-cycle of an ML project, and keeping things hands-on with small Python examples, but managing not to fall into the catalogue mode. I have seen other books try this before. "Doing Data Science" by O'Neill and Schutt comes to mind first, long on enthusiasm but a little short on quality. Then there is Manning's own "Practical Data Science with R" by Zumel and Mount. Among the three, RWML looks like a clear winner. If I had to pick on something, I would register disappointment with the book's one extended exercise, based on the NYC taxi dataset.