Want to code your own recommender system from scratch and learn machine learning theory at the same time?
Recommender systems power the platforms we use every day—Amazon, Netflix, Spotify, and more. But how do they really work? In Machine Learning: Make Your Own Recommender System, Oliver Theobald walks you through one of the most practical and fascinating applications of machine learning: personalized recommendations.
Using Python, real-world datasets, and the beginner-friendly Scikit-learn library, you’ll not only learn the theory behind collaborative filtering, content-based filtering, and hybrid approaches, but also implement them yourself—step by step.
What you’ll learn:
- The essential principles behind recommender systems
- How to set up your Python environment with Jupyter Notebook
- The difference between user-based and item-based filtering
- How to apply Singular Value Decomposition (SVD) and Naive Bayes
- Why recommendation algorithms shape online behavior—and how to build your own
This book is perfect for:
- Readers of Machine Learning for Absolute Beginners or Oliver's other data science books
- Beginners looking to learn machine learning in a hands-on way
- Readers who found the Machine Learning for Dummies book too vague
- Anyone exploring recommender system design or building portfolio projects
If you've always wanted to understand the real mechanics behind what “You might also like…” really means, this is the book for you! No PhD required—just curiosity, a computer, and the willingness to learn by doing!
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 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 using Python. 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 NotebookWorking With DataData ReductionBuilding a Collaborative Filtering ModelBuilding a Content-Based Filtering ModelEvaluationPrivacy & EthicsFuture of Recommender Systems
Please feel welcome to join this introductory course by buying a copy or sending a free sample to your preferred device.
