July 2018
Beginner to intermediate
146 pages
3h 39m
English
Content preview from Hands-On Recommendation Systems with Python
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What this book covers
Chapter 1, Getting Started with Recommender Systems, introduces the recommendation problem and the models popularly used to solve it.
Chapter 2, Manipulating Data with the Pandas Library, illustrates various data wrangling techniques using the Pandas library.
Chapter 3, Building an IMDB Top 250 Clone with Pandas, walks through the process of building a top movies chart and a knowledge-based recommender that explicitly takes in user preferences.
Chapter 4, Building Content-Based Recommenders, describes the process of building models that make use of movie plot lines and other metadata to offer recommendations.
Chapter 5, Getting Started with Data Mining Techniques, covers various similarity scores, machine learning techniques, ...
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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.