Recommendation Engines have become an integral part of any application. For accurate recommendations, you require user information. The more data you feed to your engine, the more output it can generate – for example, a movie recommendation based on its rating, a YouTube video recommendation to a viewer, or recommending a product to a shopper online. In this practical course, you will be building three powerful real-world recommendation engines using three different filtering techniques. You'll start by creating usable data from your data source and implementing the best data filtering techniques for recommendations. Then you will use Machine Learning techniques to create your own algorithm, which will predict and recommend accurate data. By the end of the course, you'll be able to build effective online recommendation engines with Machine Learning and Python – on your own.
This course is for aspiring data science professionals who need to build powerful recommendation engines for their projects. If you are a developer with no Data Science skills but want to build recommendation engines for your applications, this course will appeal to you as it assumes no prior expertise apart from a competent knowledge of Python programming.