• Post category:StudyBullet-4
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Real World Projects on recommendation systems with data science, machine learning and AI techniques..

What you will 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 โ€œrecommender systemsโ€  stand for and why are they so useful?

Letโ€™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.


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YouTube: Video dashboard

Iโ€™m sure Iโ€™m not the only one whoโ€™s accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?

Thatโ€™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 !”
English
language

Content

Introduction & welcome to this course !

Introduction to course & its benefits !
Utilize QnA section ( Golden Oppurtunity ) !
How to follow this course , must watch !
Pre-requisites (Anaconda Python & Jupter install & Set-up)
Introduction to Jupyter Notebook !

——————- Project 1 : TMDB use-case ———————-

Datasets & Resources

Build a Recommendation System using Average Weighted

Getting a High-level Overview of data..
Lets Prepare data for analysis & Model building..
Getting Overview of Average_weighted_Technique
Lets Recommend movies using Average_weighted_Technique

Build a recommendation system using Popularity Score

Lets Implement Popularity based Recommender System..

Build a recommendation system using Weighted average and Popularity score

Scaling & its different types !
How to Normalize your Data !
Lets recommend movies using Hybrid model..

Build a recommendation system using Content based filtering

Lets Understand about Content Based Recommendation system..
Applying TF-IDF on our data !
Applying Sigmoid kernel on top of our data !
How to design a function from scratch in real-world !
Lets Build Content_based model..

Build a more Advance recommendation system using Content based filtering

Understand how to improve Model from business perspective !
Lets perform Feature Extraction !
Lets clean & prepare our data
What is Meta-data & how to Create meta-data..
Lets Recommend Movies..

——————- Project 2 : Movie_lens use-case ——————–

intro

Build a Recommender System using Co-rrelation

Lets Perform Data Preparation..
Applying Statistical Approaches on Data !
What is Pivot_Table & how to create it ?
Lets Build recommender system using Co-relation.

Build a Recommender System using KNN-based Collaborative filtering..

Lets explore our data..
Lets Build KNN based collabarative model..
How to make your Recommendations more Interactive !