Recommender systems are algorithms that suggest relevant items to users (movies, books, products, or a service). Recommender systems are critical in specific industries to generate massive incomes efficiently or stand out significantly from competitors. The course begins with basic recommender system concepts. You will learn important recommender system taxonomies and recommender system mechanism development using machine and deep learning with Python. Python as a programming language will be taught in this course to implement machine and deep learning concepts efficiently. You will model a k-nearest neighbor-based recommender engine for various applications and know the pros and cons of deep learning-based mechanisms. You will build a recommender system for apps such as Spotify and explore neural collaborative filtering and variational auto-encoders for collaborative filtering. You will explore various matrices (item context, user rating, and error). You will understand recommender system quality, online/offline evaluation techniques, dataset partitioning, and overfitting. Upon completing the course, you will understand the roles and impacts of recommender systems in real-world applications with a unique hands-on experience in developing complete recommender system engines for customized datasets in various projects. All resources are available at: https://github.com/PacktPublishing/Recommender-Systems-Complete-Course-Beginner-to-Advance