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Book Review: Machine Learning in Action by Peter Harrington

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Book Review: Machine Learning in Action by Peter Harrington

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A couple months ago I briefly reviewed Machine Learning for Hackers by Drew Conway and John Myles White. Today I’m looking at Machine Learning in Action by Peter Harrington and comparing the two books.

Both books are about the same size and many of the same topics. One difference between the two books is choice of programming language: ML for Hackers uses R for its examples, ML in Action uses Python.

ML in Action doesn’t lean heavily on Python libraries. It mostly implements its algorithms from scratch, with a little help from NumPy for linear algebra, but it does not use ML libraries such as scikit-learn. It sometimes uses Matplotlib for plotting and uses Tkinter for building a simple GUI in one chapter. The final chapter introduces Hadoop and Amazon Web Services.

ML for Hackers is a little more of a general introduction to machine learning. ML in Action contains a brief introduction to machine learning in general, but quickly moves on to specific algorithms. ML for Hackers spends a good number of pages discussing data cleaning. ML in Action starts with clean data in order to spend more time on algorithms.

ML in Action takes 8 of the top 10 algorithms in machine learning (as selected by this paper) and organizes around these algorithms. (The two algorithms out of the top 1o that didn’t make it into ML for Hackers were PageRank, because it has been covered well elsewhere, and EM, because its explanation requires too much mathematics.) The algorithms come first in ML in Action, illustrations second. ML for Hackers puts more emphasis on its examples and reads a bit more like a story. ML in Action reads a little more like a reference book.

Compliant Database DevOps and the role of DevSecOps DevOps is becoming the new normal in application development, and DevSecOps is now entering the picture. By balancing the desire to release code faster with the need for the same code to be secure, it addresses increasing demands for data privacy. But what about the database? How can databases be included in both DevOps and DevSecOps? What additional measures should be considered to achieve truly compliant database DevOps? This whitepaper provides a valuable insight. Get the whitepaper

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