Book
Machine Learning: Make Your Own Recommender System
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Paperback
$12.90
Recommender systems are one of the most visible applications of machine learning and their uncanny ability to convert our unspoken actions into items we like is both addicting and concerning. Recommender systems, though, 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 using Python. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. Who is the Book For?
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:
- How to set up a free and easy sandbox environment using Jupyter Notebook
- How to prepare your data for processing
- How to code a collaborative filtering model
- How to code a content-based filtering model
- How recommender systems are evaluated
- What you need to know about privacy and ethics
- What the future of Recommender Systems might look like
Recommender systems are one of the most visible applications of machine learning and their uncanny ability to convert our unspoken actions into items we like is both addicting and concerning. Recommender systems, though, 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 using Python. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. Who is the Book For?
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:
- How to set up a free and easy sandbox environment using Jupyter Notebook
- How to prepare your data for processing
- How to code a collaborative filtering model
- How to code a content-based filtering model
- How recommender systems are evaluated
- What you need to know about privacy and ethics
- What the future of Recommender Systems might look like
Paperback
$12.90