In today's digital landscape, personalized experiences have become the norm. Whether it's Netflix suggesting a show or an online retailer recommending items, these tailored suggestions have become an integral part of our online interactions. However, have you ever wondered how these recommendations are generated? In our comprehensive course, we will equip you with all the necessary knowledge and skills to create your own recommendation engine. Through practical exercises, you will gain a deep understanding of the two most commonly used systems: collaborative filtering and content-based filtering. Furthermore, you will learn how to measure similarities using advanced techniques such as the Jaccard distance and cosine similarity. Additionally, we will delve into evaluating the quality of recommendations by utilizing the root mean square error (RMSE) on test data. By the conclusion of this course, you will have successfully constructed your very own movie recommendation engine. Moreover, you will possess the ability to apply your Python programming skills to develop these systems for any industry.