Machine Learning Make Your Own Recommender System

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

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