December 2016
Beginner
388 pages
8h 35m
English
Content preview from Principles of Data ScienceBecome 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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Chapter 6. Advanced Probability
In the previous chapter, we went over the basics of probability and how we can apply simple theorems to complex tasks. To briefly summarize, probability is the mathematics of modeling events that may or may not occur. We use formulas in order to describe these events and even look at how multiple events can behave together.
In this chapter, we will explore more complicated theorems of probability and how we can use them in a predictive capacity.
Advanced topics, such as Bayes theorem and random variables, give rise to common machine learning algorithms, such as the Naïve Bayes algorithm (also covered in this book). This chapter will focus on some of the more advanced topics in probability theory, including the following ...
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