The world of eCommerce is filled with products and services. In this course, you’ll learn to process large amounts of data and recommend the correct products.
When dealing with a database of thousands of products or billions of songs and videos, how do you decide which ones your clients and customers will be most interested in? In this course, Crafting Intelligent Recommendation Systems with Deep Learning, you’ll gain the ability to understand, maintain, and contribute to deep learning LLMs capable of providing excellent recommendations. First, you’ll learn to understand recommendation systems, how they work, and what they’re capable of doing. Next, you’ll discover the predictive and performative power of matrix factorization, embeddings, and other key, high-level ML recommendation system concepts. Finally, you’ll learn how to optimize and evaluate ML recommendation systems to maximize performance and minimize costs. When you’re finished with this course, you’ll have the skills and knowledge of recommendation systems and deep learning needed to contribute to and continue building mastery in ML engineering and recommendation systems.
When dealing with a database of thousands of products or billions of songs and videos, how do you decide which ones your clients and customers will be most interested in? In this course, Crafting Intelligent Recommendation Systems with Deep Learning, you’ll gain the ability to understand, maintain, and contribute to deep learning LLMs capable of providing excellent recommendations. First, you’ll learn to understand recommendation systems, how they work, and what they’re capable of doing. Next, you’ll discover the predictive and performative power of matrix factorization, embeddings, and other key, high-level ML recommendation system concepts. Finally, you’ll learn how to optimize and evaluate ML recommendation systems to maximize performance and minimize costs. When you’re finished with this course, you’ll have the skills and knowledge of recommendation systems and deep learning needed to contribute to and continue building mastery in ML engineering and recommendation systems.
