Recommender Systems with Machine Learning

This course starts with the theoretical concepts and fundamental knowledge of recommender systems, covering essential taxonomies. You'll learn to use Python to evaluate datasets based on user ratings, choices, genres, and release years. Practical approaches will help you build content-based and collaborative filtering techniques. As you progress, you'll cover necessary concepts for applied recommender systems and machine learning models, with projects included for hands-on experience. Key learnings include AI-integrated basics, taxonomy, overfitting, underfitting, bias, variance, and build…