Build Recommendation Algorithms - Machine Learning to Trigger Clicks in Python
Learn how to create your own movie recommender systems using real-world datasets, user behavior, and advanced machine learning techniques. This hands-on course walks you through the complete process—from data preprocessing to user similarity modeling, KNN classifiers, and neural network architectures.
This course includes.
Curriculum & lectures.
+ Welcome! 1 lecture
+ 00a Recommender System Types & Performance Metrics 3 lectures
+ 00b Data Structures & Feedback Loops 3 lectures
+ 01 Introduction to Recommender Systems 5 lectures
+ 02 Build a Basic Movie Recommender System 5 lectures
+ 03 Build a Simple Movie Recommender with Machine Learning 1 lecture
+ 04 Introduction to User Similarity 5 lectures
+ 05 Recommend a Movie Based on User Similarity 4 lectures
+ 06 Recommend a Movie with a K Nearest Neighbors Classifier 5 lectures
+ 07 Machine Learning User Recommendations with Profiles and Items 1 lecture
+ 08 Data Processing Profiles and Items 4 lectures
+ 09 Build Models for User Recommendations 9 lectures
+ 10 Build a Model to Predict Ratings 4 lectures
+ 11 Build a Dense Neural Network to Recommend Movies 3 lectures
+ 12 Build a Neural Network to Predict Ratings 3 lectures
+ 13 Data Analysis with Pandas, Numpy and Sci-kit Learn 9 lectures
About this course.
✅ Understand how recommender systems work and their real-world applications
✅ Build a basic content-based recommender system with Python
✅ Apply machine learning to create personalized user-based recommendations
✅ Use K-Nearest Neighbors to predict user preferences based on behavior and similarity
✅ Process and analyze data using Pandas, NumPy, and Scikit-learn
✅ Create profile-based recommendation models by connecting users to items
✅ Build, train, and evaluate dense neural networks for scalable movie suggestions
✅ Work with user-item matrices and collaborative filtering strategies
🎮 Follow step-by-step projects that build your skills from simple logic to machine learning-powered predictions.
🎁 Includes source code, sample datasets, and lifetime access with free updates.
If you're ready to master the technology behind Netflix and Spotify’s recommendation engines—this course will show you how. Enroll now and start building intelligent recommender systems today.
Taught by people who ship.
Alex Kropf
Mammoth Club's CLO, public speaker, consultant, IT author and Senior Software Developer. Alex has produced best-selling courses, books and workshops for Mammoth Club, Course Pro and our clients since 2016.
Ready to start building?
Learn how to create your own movie recommender systems using real-world datasets, user behavior, and advanced machine learning techniques. This hands-on course walks you through the complete process—from data preprocessing to user similarity modeling, KNN classifiers, and neural network architectures.