Building Recommender Systems with Machine Learning and AI

In this 11-hour course, you'll master the design and implementation of recommender systems using Python and machine learning. You'll build up from basic understanding to creating complex hybrid and scaleable systems, leveraging real-world methods used by industries like Netflix and YouTube.

What I will be able to do after this course

• Understand and implement basic recommender system architectures and principles.
• Apply machine learning algorithms to evaluate and improve recommendation accuracy.
• Develop content-based filtering and collaborative filtering techniques for tailored suggestions.
• Integrate deep learning frameworks for more advanced recommendation functionality.
• Scale recommendation engines with large datasets efficiently using tools like Apache Spark.

Course Instructor(s)

Frank Kane is a seasoned AI and machine learning expert with hands-on industry experience. With a background in developing large-scale recommendation systems at Amazon and IMDb, Frank translates this expertise into an engaging teaching style that is both practical and accessible.

Who is it for?

This course is ideal for software engineers and data scientists keen on exploring recommender system design. Suitable for enthusiasts with some Python programming and basic algorithm understanding, it appeals to professionals aiming to enrich their technical toolbox with hands-on industry-relevant skills.