November 2023
Beginner
464 pages
11h 38m
English
Content preview from Python for Data Science For Dummies, 3rd Edition
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Chapter 17
Exploring Four Simple and Effective Algorithms
IN THIS CHAPTER
Using linear and logistic regression
Understanding Bayes’ theorem and using it for naive classification
Predicting on the basis of cases being similar with KNN
In this new part of the book, you start to explore algorithms and tools necessary for learning from data, meaning a training a model, and being capable of predicting a numeric estimate (such as house pricing in some areas of California) or a class (such as the species of penguins that can be found in the Palmer Archipelago in Antarctica) given any new example that you didn’t have before. In this chapter, you start with the simplest algorithms and work toward those that are more complex. The four algorithms in this chapter represent a good starting point for any data scientist.
You don’t have to type the source code for this chapter manually; using the downloadable source is a lot easier (see the Introduction for download instructions). The source code for this chapter appears in the P4DS4D3_17_ Exploring_Four_Simple_and_Effective_Algorithms.ipynb file. ...
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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.