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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 building content-based and item-based systems with ML and Python, including KNN-based engines. The course is suitable for beginners and those with some programming experience, aiming to advance ML skills and build customized recommender systems. No prior knowledge of recommender systems, ML, data analysis, or math is needed, only basic Python. By the end, you'll relate theories to various domains, implement ML models for real-world recommendation systems, and evaluate them.