Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.83 GB | Duration: 3h 36m
Real World Projects on recommendation systems with data science, machine learning and AI techniques..
What you’ll learn
Learn How to tackle Real world Problems..
Learn Collaborative based filtering
Learn how to use Correlation for Recommending similar Movies or similar books
Learn Content based recommendation system
Learn how to use different Techniques like Average Weighted , Hybrid Model etc..
Learn different types of Recommender Systems
Description
Believe it or not, almost all online platforms today uses recommender systems in some way or another.
So What does “recommender systems” stand for and why are they so useful?
Let’s look at the top 3 websites on the Internet : Google, YouTube, and Netfix
Google: Search results
Thats why Google is the most successful technology company today.
YouTube: Video dashboard
I’m sure I’m not the only one who’s accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?
That’s right this is all on account of Recommender systems!
Netflix: So powerful in terms of recommending right movies to users according to the behaviour of users !
Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them.
This course gives you a thorough understanding of the Recommendation systems.
In this course, we will cover
Use cases of recommender systems.
Average weighted Technique Recommender System
Popularity-based Recommender System
Hybrid Model based on Average weighted & Popularity
Collaborative filtering.
Content based filtering
and much, much more!
Not only this, you will also work on two very exciting projects.
Instructor Support – Quick Instructor Support for any query within 2-3 hours
All the resources used in this course will be shared with you via Google Drive Link
How to make most from the course ?
Check out the lecture “Utilize This Golden Oppurtunity , QnA Section !”
Who this course is for
Data Scientists
Data Analysts
Machine learning Engineer
Anyone who wants to deep dive into data science.
Students and Professionals who want to gain Hands-on..
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