Build ML-Powered Recommender Systems with Python

In this module, we will introduce you to the field of AI Sciences and recommender systems. You will meet the instructor, explore the course layout, understand the basics of recommender systems, and preview the exciting projects you will undertake.

In this module, we will delve into the motivations behind recommender systems. You will learn about their processes, historical evolution, and the critical role AI plays. We'll also cover practical applications and the challenges faced in real-world scenarios.

In this module, we will cover the foundational aspects of recommender systems. You will study the taxonomy, data matrices, evaluation techniques, and filtering methods, equipping you with a solid understanding of how these systems function and are assessed.

In this module, we will focus on leveraging machine learning for recommender systems. You will gain insights into data preparation, explore filtering methods, and implement machine learning algorithms like tf-idf and KNN, enhancing the recommendation process.

In this module, we will guide you through building a song recommendation system using content-based filtering. You will work on dataset management, genre exploration, and implement advanced techniques like tf-idf and FuzzyWuzzy to create effective song recommendations.

In this module, we will take you through developing a movie recommendation system using collaborative filtering. You will learn to analyze user and movie data, create collaborative filters, and apply KNN to generate accurate movie recommendations, culminating the course with practical applications.