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Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 349 MB
Genre: eLearning Video | Duration: 15 lectures (59 mins) | Language: English
Learn to apply recommendation techniques used by Amazon, Netflix, Youtube, IMDB
What you'll learn
Learn Collaborative Filtering Recommendation technique
Learn Content Based Filtering Recommendation technique
Learn to build Hybrid Recommendation Engine
Learn the techniques used by Amazon, Netflix to recommend products to the customer
Learn the fundamental concepts about Recommendation Engine
Course content
7 sections * 15 lectures * 59m total length
Requirements
Anaconda installed in pc
Python installed in pc
Little bit knowledge of python programing, pandas and numpy
Description
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.
Who this course is for:
any machine learning engineer or data scientist who want to learn about trending machine learning application
any professional who want to know the secrets behind the recommendation of the products
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