The Hard Part is Motivation. Books. &c

Building Machine Learning Systems with Python

Because my book just came out, I am again excerpting from a Portuguese interview with me I mentioned previously:

Who is the target audience for this book?

Luis Pedro: There are two distinct audiences: The first are programmers who do not know much of machine learning, but liked to use a classifier, for example. The second are people who do not need an introduction to machine learning (because they already know), but maybe do not know how to do it with the tools in Python.

What were the main difficulties encountered in this process?

Luis Pedro: The challenge is always to find examples that are not too easy, but they are not also too difficult for an introduction. In one case (image classification), I took some photographs to create a dataset. In the literature, there are classical examples which are trivial to handle with modern techniques and current research problems that are very difficult. My dataset consists of poorly framed photographs taken with a mobile phone camera. Therefore, it also has a style more akin to a problem area or mobile web. The aim is to distinguish photographs of buildings, natural landscape, or texts.

There was nothing too complex for anyone who knows the area. In fact, when it comes to writing about something you already know how to do (as was the case), the hard part is the motivation.


One thing we stress throughout is principled evaluation and we warn against overselling your results. Even in the world of research, we still find papers that mix training set and test set! Obviously not coming from groups that work in machine learning, but in more applied areas, we still find people who test hyper-parameters in the test set.

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