Machine Learning: Make Your Own Recommender System

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!