October 2016
Beginner
378 pages
8h 21m
English
Content preview from Python Data Science Essentials - Second EditionBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Summary
In this chapter, we introduced the essentials of machine learning. We started with some easy, but still quite effective, classifiers (linear and logistic regressors, Naive Bayes, and K-Nearest Neighbors). Then, we moved on to the more advanced ones (SVM). We explained how to compose weak classifiers together (ensembles, Random Forests, and Gradient Tree Boosting). Finally, we had a peek at the algorithms used in big data, clustering, and deep learning.
In the next chapter, you'll be introduced to graphs, which is an interesting deviation from the predictors/target flat matrices. It is quite a hot topic in data science now. Expect to delve into very complex and intricate networks!
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.