Bookshelf: Machine Learning and Data Science

With less options in your sparetime in the current situation, we can invest some of the extra time we have gained in reading instead watching (too much) Netflix and other streaming services. Here some recommendations from my booskhelf. Machine Learning and the related topics like Data Science are the hot topics in IT in almost any business domain context and a basic understanding is important for everyone dealing with software, be it sales reps or product managers with little or no computer science background, and the last time you touched discrete mathematics, linear algebra or probability was at high school. The challenge here, both topics have a steep learning curve and it is hard to acquire a decent knowledge, but for most of us, basic understanding is good enough to manoeuvre discussions and appreciate what is possible and what not (now). There is a huge choice of online courses and books, though the majority is most likely to deep-dive or covering a very specific aspect of machine learning. Publisher like Springer and Packt even allow you download selected titles these days or permanently for free.
Here 2 titles that scrape the surface but give you some insights and overview of commonly used terms. Both available as paperbacks or for the Kindle for less than Euro 15,-.

Machine Learning For Absolute Beginners

by Oliver Theobald, Independently published, 2018, 155 pages

If you start from ground zero with no knowlegde, this is a good overview at 10.000 feet without diving too much into formulars and algorithms. You learn about linear and logistic regression, decision trees, clustering and more but just enough to get a basic understanding. Neural networks are covered too, but it might be challenging to transport this through only 10 pages. There is a bit of Python coding sprinkled in if you feel getting more hands-on.

Data Science

by John Kelleher, The MIT Press Essential Knowledge, 2018, 280 pages

This book covers the basics of data science, ranging from the definition of data types and the DIKW pyramid to standard tasks and outlining the whole data science steps in the CRISP-DM process. Non-technical aspects like ethics and privacy are covered too. This book is 99% free of algorithm and sourcecode.

The MIT Press Essential Knowledge series offers a few condensed titles, like Deep Learning, MetaData, Computational Thinking and others.

I will recommend some more titles soon. Stay safe and tuned.

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