Python: Real World Machine Learning

Summary

We've been introduced to a lot of useful and highly applicable skills in this chapter. In this chapter, we took a set of messy, complication-strewn text data and, through a series of rigorous steps, turned it into a large set of effective features. We began by picking up a set of data cleaning skills which stripped out a lot of the noise and problem elements, then we followed up by turning text into features using POS tagging and bag of words. In the process, you learned to apply a set of techniques that are widely applicable and very empowering, enabling us to solve difficult problems in many natural language processing contexts.

Through experimentation with multiple individual models and ensembles, we discovered that where a smarter algorithm ...