Enroll Course: https://www.coursera.org/learn/basic-recommender-systems
In today’s data-driven world, personalized experiences are no longer a luxury but a necessity. From streaming services suggesting your next binge-watch to e-commerce platforms recommending products you’ll love, recommender systems are at the heart of it all. If you’re curious about how these powerful engines work or looking to build your own, Coursera’s “Basic Recommender Systems” course is an excellent starting point.
This course offers a comprehensive introduction to the leading approaches in recommender systems, covering both collaborative and content-based techniques. It delves into the most important algorithms, explaining not just how they function but also how to implement and evaluate them, highlighting the strengths and limitations of various methods.
The syllabus is thoughtfully structured, beginning with “Basic Concepts.” Here, you’ll grasp the foundational principles, enabling you to classify and analyze different algorithm families based on their input data. This module is crucial for understanding which algorithm best suits your specific needs and data, or conversely, how to prepare your data for a chosen algorithm.
Next, the course tackles the vital aspect of “Evaluation of Recommender Systems.” You’ll learn to define and measure the quality of a recommender system, exploring various metrics. By the end of this section, you’ll be equipped to select the appropriate evaluation activities to gauge performance against your goals.
Two core modules form the backbone of the course: “Content-Based Filtering” and “Collaborative Filtering.” In the content-based module, you’ll explore algorithms that recommend items similar to those a user has liked previously. You’ll learn about different similarity functions, how to choose the most suitable one, and the importance of the Item-Content Matrix (ICM). The course guides you through cleaning and normalizing input data and implementing tuning strategies to optimize recommendations.
The “Collaborative Filtering” module shifts focus to techniques using the User Rating Matrix (URM). You’ll discover how to build non-personalized recommenders, normalize the URM for better results, and select appropriate similarity functions. This section also addresses common challenges with explicit ratings.
Overall, “Basic Recommender Systems” provides a solid theoretical foundation and practical insights into building effective recommender systems. It’s ideal for anyone interested in machine learning, data science, or enhancing user experience through personalization. Highly recommended for beginners and those looking to solidify their understanding of this fascinating field.
Enroll Course: https://www.coursera.org/learn/basic-recommender-systems