July 2018
Beginner to intermediate
146 pages
3h 39m
English
Content preview from Hands-On Recommendation Systems with Python
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Summary
With this, we come to the end of this chapter, as well as the main part of the book. In this book, we learned the following:
- We were introduced to the world of recommender systems. We defined the recommendation problem mathematically and discussed the various types of recommendation engines that exist, as well as their advantages and disadvantages.
- We then learned to perform data wrangling with the pandas library and familiarized ourselves with two of pandas, most powerful data structures: the series and the DataFrame.
- With our newly found data wrangling techniques, we proceeded to build an IMDB Top 250 clone. We then improved on this model to build a knowledge-based recommender that took into account the recommended movies' genre, ...
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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.