Machine Learning on a Cancer Dataset - Part 35

Leaving the cancer dataset aside, we're gonna use the iris dataset and other sets of data in the following few videos where we discuss methods of preprocessing with scikit-learn.

The first method that we're going through is 'binarization'. It often is very helpful and process efficient to turn numeric and even text values into zeroes and ones. This can make the entire ML workflow smoother.

You can also convert data back-and-forth, meaning that once you trained an algorithm and you used binarization, you could convert data back into text values for example and use it for interpretation, and not only.

In this video tutorial, we're going very slowly through binarization using dummy data. Other methods of preprocessing are to be described in upcoming videos. I hope this is helpful.

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Previous videos in this series:

1. Machine Learning on a Cancer Dataset - Part 30
2. Machine Learning on a Cancer Dataset - Part 31
3. Machine Learning on a Cancer Dataset - Part 32
4. Machine Learning on a Cancer Dataset - Part 33
5. Machine Learning on a Cancer Dataset - Part 34

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Cristi Vlad, Self-Experimenter and Author