Description

In today's digital landscape, recommendation systems power many personalized experiences we encounter daily, from Netflix's content suggestions to Spotify's music playlists. Our two-day intensive course, Building Recommender Systems Using Python, offers a deep dive into the world of data-driven personalization. You'll begin by exploring the core concepts and types of recommendation systems, understanding how they function to tailor content for individual users. From there, you'll engage with hands-on activities, setting the foundation for building your own recommenders. On the first day, you'll work extensively with the Pandas library, learning how to manipulate and prepare data for recommendation systems. Through guided labs, you will build simple and knowledge-based recommenders and advance to creating sophisticated content-based recommenders using document vectors, cosine similarity, and metadata analysis. On day two, the course transitions to advanced data mining techniques, covering clustering, dimensionality reduction, and various similarity measures. You will also dive into collaborative filtering, learning both user-based and item-based approaches to improve recommendation accuracy. The course culminates in a hands-on session where you'll deploy your recommender as a microservice using Docker, allowing for real-world application and scalability. By the end of the program, you'll have mastered the tools and techniques necessary to design, implement, and optimize effective recommendation systems, enabling you to elevate user experiences, boost engagement, and drive smarter decision-making on digital platforms.