Building Recommender Systems with Machine Learning and AI

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you'll explore the inner workings of recommender systems, gaining hands-on experience with Python and various machine learning techniques. Starting with the basics, you'll quickly move to more advanced methods like content-based filtering, collaborative filtering, and matrix factorization. By building real-world systems, you'll develop the skills needed to evaluate and improve recommender system performance. As you advance, you'll dive into deep learning for recommender systems, experimenting with technologies like Restricted Boltzmann Machines (RBM) and Autoencoders. You'll also explore TensorFlow Recommenders and other state-of-the-art approaches for building scalable recommendation engines. This course is designed to help you build, test, and deploy sophisticated recommender systems that can be applied in various industries. This course is ideal for those interested in artificial intelligence, machine learning, and data science, especially those who want to build personalized systems to enhance user experience. It will benefit anyone looking to design, evaluate, and optimize recommendation algorithms, making it an excellent resource for aspiring data scientists, machine learning engineers, and AI specialists.