Introduction to Recommendation Systems with Python

Ever wonder how streaming services suggest your next favorite movie or how e-commerce sites know what products you might like? This course demystifies the core technology behind the personalized experiences that power the modern web. You will move from foundational theory to practical application, learning the essential principles of recommendation systems. By the end, you'll have built your own functional movie recommender using Python and common data science libraries, gaining hands-on experience in one of the most impactful areas of machine learning. What you'll learn: - Understand the fundamental principles behind collaborative and content-based filtering. - Set up a dedicated Python environment for your data science projects. - Use the pandas library to load, clean, and prepare datasets for analysis. - Build a collaborative filtering model to recommend items based on user behavior. - Create a content-based recommender by analyzing the features of the items themselves. - Learn to measure the effectiveness of your models with basic evaluation metrics. - Apply all concepts to build a complete, working movie recommendation engine. The course starts with key terminology and the core ideas behind recommendation engines before guiding you through the practical steps of data handling and model building. You'll follow clear, text-based explanations and practice with hands-on exercises. This course is designed for beginners. No prior experience with machine learning is necessary, though a basic familiarity with Python syntax is beneficial. Start your journey into building intelligent, personalized systems today.