This course will guide you through building and evaluating recommender systems using Python. You鈥檒l start with the basics of recommender systems and Python programming, progressing through techniques like content-based filtering, collaborative filtering, and matrix factorization. By the end of the course, you'll have the skills to create real-world recommender systems and evaluate their performance. The course starts with an introduction to Python and recommender systems, covering types of recommenders, implicit vs. explicit ratings, and top-N architecture. You'll learn to evaluate systems using metrics like RMSE, MAE, hit rates, and diversity, and understand concepts like churn and novelty. As the course progresses, you'll explore content-based filtering and K-Nearest-Neighbors (KNN). Then, you鈥檒l dive into collaborative filtering methods, including user-based and item-based techniques. You鈥檒l also gain hands-on experience implementing these methods using Python and datasets like MovieLens. This course is perfect for learners with basic Python knowledge who want to dive into machine learning applications in recommendation systems. It鈥檚 ideal for those interested in creating, evaluating, and deploying recommender systems.
Measuring the Hit Rate of Item-based Collaborative Filtering (Exercise)
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Skills You'll Learn
Matrix Factorization, Collaborative Filtering, Recommender Systems, Data preprocessing and feature engineering, System Evaluation and Optimization, Deep Learning for Recommendations, Content-based Filtering, Python Programming, Recommender System Design
From the lesson
Neighborhood-Based Collaborative Filtering
In this module, we will cover neighborhood-based collaborative filtering techniques, including user-based and item-based methods. Practical exercises and activities will help solidify your understanding of these approaches.
Taught By
Packt - Course Instructors