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Learn to apply recommendation techniques used by Amazon, Netflix, Youtube, IMDB
Updated on Sep, 2026
Programming, Data Science, Machine Learning
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In this course, you'll going to learn about a recommendation system. Also known as recommender engines. According to Netflix, 70% of the videos are seen by recommending videos to the user. Not only Netflix, but Amazon also claims most products, because of their recommendation system. There is a wide range of techniques to be used to build recommender engines. This learning path will mostly cover all the easy-to-moderate kinds of techniques with hands-on experience.
What is a Recommendation System?
Recommender systems aim to predict users’ interests and recommend product items that are quite likely to be interesting for them. They are among the most powerful machine learning systems that online retailers implement in order to drive sales. Data required for recommender systems stems from explicit user ratings after watching a movie or listening to a song, from implicit search engine queries and purchase histories, or from other knowledge about the users/items themselves.
Two types of Recommendation systems are Collaborative Based and Content-based filters, Recommending systems. You'll excel in both methods after the completion of the course. Other than this, you'll also learn more about cosine, Pearson correlation, as well as different types of machine learning algorithms like Logistic regression and K-nearest neighbours to get the best recommendation.
Check out the detailed breakdown of what’s inside the course
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