January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Summary
In this chapter, we have studied association rules. We also discussed the Apriori algorithm, which is used for mining frequent itemsets to derive various association rules. We also learned about frequent pattern growth (FP-growth), which is similar to Apriori and about the frequent itemset generation technique, which is similar to the Apriori algorithm. Finally, we saw how FP-growth tends to have an edge over Apriori, as it is faster and more efficient, using an example.
In the next chapter, we will study probabilistic graphical models. We will learn in depth about the Bayesian rules and Bayesian networks.
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.