Learn about recommendation system. Also known as recommender engines. Recommendation Engines are everywhere. Netflix, Amazon and YouTube to name a few. Then there is the Ultimate Recommendation Engine: Google. Recommendation Engines help us make choices suited to our personal tastes.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 themselves. The object of this course is for you to walk away with a solid understanding of the fundamentals behind the Collaborative filtering algorithm used by companies like Netflix or Amazon Prime to recommend movies to users based on the tastes of other similar users. 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.

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.

Recommendation Engines will be essential to selling anything and Big Companies are already looking on new ways to use them and for developers and marketeers who understand them. This course will give you a fundamental, conceptual understanding of how Recommendation Engines work by walking you through building a simple toy Recommendation Engine from scratch using simple math and basic python programming skills. Taking this course is an easy way to prepare for more advance study as concepts are explained in plain language and code is walked through line by line.