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Hands-On Recommendation Systems with Python

You're reading from Hands-On Recommendation Systems with Python Start building powerful and personalized, recommendation engines with Python

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788993753

Length 146 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Rounak Banik

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Table of Contents (9) Chapters

Preface

1. Getting Started with Recommender Systems FREE CHAPTER

2. Manipulating Data with the Pandas Library

3. Building an IMDB Top 250 Clone with Pandas

4. Building Content-Based Recommenders

5. Getting Started with Data Mining Techniques

6. Building Collaborative Filters

7. Hybrid Recommenders

8. Other Books You May Enjoy

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Summary

In this chapter, we gained an understanding of the limitations of using vanilla Python and its built-in data structures. We acquainted ourselves with the Pandas library and learned how it overcomes the aforementioned difficulties by giving us access to extremely powerful and easy-to-use data structures. We then explored the two main data structures, Series and DataFrame, by analyzing our movies-metadata dataset.

In the next chapter, we will use our newfound skills to build an IMDB Top 250 Clone and its variant, a type of knowledge-based recommender.

The rest of the chapter is locked

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Authors (1)

Banik

Banik

Rounak Banik is a Young India Fellow and an ECE graduate from IIT Roorkee. He has worked as a software engineer at Parceed, a New York start-up, and Springboard, an EdTech start-up based in San Francisco and Bangalore. He has also served as a backend development instructor at Acadview, teaching Python and Django to around 35 college students from Delhi and Dehradun. He is an alumni of Springboard's data science career track. He has given talks at the SciPy India Conference and published popular tutorials on Kaggle and DataCamp.

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