Practical Machine Learning Books for the Holidays
Last Updated on August 16, 2020
O’Reilly books have a reputation for being practical, hands on and useful. Specifically the nutshell books and so-called animal books.
O’Reilly have a few new books out in time for the holidays on the topic of machine learning.
I don’t want to bore you with reviews, Amazon has plenty of those. In this post we take a quick look at these new machine learning books and see what might be worth reading in the holiday period.
Written by Matthew Kirk and released in October 2014.
Thoughtful Machine Learning- A Test-Driven Approach
Learn machine learning by implementing algorithms from scratch and verify the implementations using test-driven development.
Covers algorithms like
- K-Nearest Neighbors for classification
- Naive Bayes for Classification
- Hidden Markov Models
- Support Vector Machines
- Neural Networks
- K-Means and Expectation Maximization for clustering
- Kernel Ridge Regression
Surprisingly it uses Ruby rather than Python (the same choice I made for Clever Algorithms).
It looks like fun and might well be suited to those interested in jumping
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