Machine Learning: third book

I am deviating a bit from the originally planned order of reading books as well as from the topic of TensorFlow, but I picked up the next book "Python - Machine Learning" by Wei-Meng Lee anyway from the local bookstore, since it presents some basic technologies that are relevant to ML. As the introduction states, the book takes a gentle approach to the ML topic, but it provides an interesting read nevertheless. First some fundamental Python libraries are presented: NumPy (multi-dimensional arrays), Pandas (Panel Data Analysis), matplotlib/Searborn (data visualization), and Scikit-learn (ML algorithms for classification, regression, clustering, decision tree, etc.). The book provides resources for data sets, discusses data cleaning, and goes over several ML examples for supervised learning (one regression chapter and three classification chapters) and unsupervised learning (one clustering chapter). The book finishes with a presentation of the Azure Machine Learning Studio (which I skimmed) and how to deploy ML models once the training is complete.

If you are already very familiar with ML, perhaps other books serve your purpose better. But as an introduction to the topic, however, I can warmly recommend the book since the discussion of the ML technologies is interesting and the artwork is clear (with one unfortunate exception: some graphs that were originally in color are printed in black and white, making  distinguishing different data points hard).


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