March 2016
Beginner to intermediate
340 pages
7h 20m
English
Content preview from R Machine Learning By ExampleBecome 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 continued our pursuit of using machine learning in the field of e-commerce to enhance sales and overall user experience. The previous chapter had discussed recommendations based on transactional logs; in this chapter, we accounted for the human factor and looked into the recommendation engines based on user behavior.
We started off by understanding what recommendation systems and their classifications into user-based, content-based, and hybrid recommender systems. We touched on the problems associated with recommender engines in general. Then we dived deep into the specifics of collaborative filters and discussed the math around prediction and similarity measures. After getting our basics straight, we moved onto building ...
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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.