English | June 20, 2018 | ASIN: B07DWS346Y | 121 pages | AZW3 | 2.15 MB

Learn How to Make Your Own Recommender System in an Afternoon.

Recommender systems are one of the most visible applications of machine learning and data mining today and their uncanny ability to convert our unspoken actions into presenting items we desire is both addicting and concerning. And whether recommender systems excite or scare you, the best way to manage their influence and impact is to understand the architecture and algorithms that play on your personal data. Recommender systems are here to stay and for anyone beginning their journey in data science, this is a lucrative space for future employment.

This book will get you up and running with the basics as well as the steps to coding your own recommender system. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming. If this is your first exposure to data science, you may want to spend a few hours to read my first book Machine Learning for Absolute Beginners before you get started here.

Topics covered in this book:

Setting Up A Sandbox Environment With Jupyter Notebook

Working With Data

Data Reduction

Building a Collaborative Filtering Model

Building a Content-Based Filtering Model

Evaluation

Privacy & Ethics

Future of Recommender Systems