In this course, you'll going to learn about recommendation system. Also known as recommender engines. According to Netflix, there 70% of the videos seen by recommending the videos to the user. Not only Netflix, Amazon also claims most products, they because of their recommendation system. There is a wide range of techniques to be used to build recommender engines. In this learning path, It will mostly cover all the easy to moderate kind of techniques with hands on experience.

What is Recommendation System?

Recommender systems aim to predict users’ interests and recommend product items that quite likely are 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 system. You'll be excel both the methods after the completion of course. Other than this you'll also learn more about cosine, Pearson correlation as well different types of machine learning algorithms like Logistic regression and K-nearest to get the best recommendation.

What you'll learn in this course?

• Fundamental concepts about Recommendation Engine

• Collaborative Filtering Recommendation

• Content Based Filtering Recommendation

• Hybrid Recommendation Engine