Recommendation system Real World Projects using Python

Recommendation system Real World Projects using Python, available at $64.99, has an average rating of 3.9, with 39 lectures, based on 42 reviews, and has 2005 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists or Data Analysts or Machine learning Engineer or Anyone who wants to deep dive into data science. or Students and Professionals who want to gain Hands-on.. It is particularly useful for Data Scientists or Data Analysts or Machine learning Engineer or Anyone who wants to deep dive into data science. or Students and Professionals who want to gain Hands-on..

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Summary

Title: Recommendation system Real World Projects using Python

Price: $64.99

Average Rating: 3.9

Number of Lectures: 39

Number of Published Lectures: 37

Number of Curriculum Items: 39

Number of Published Curriculum Objects: 37

Original Price: ?799

Quality Status: approved

Status: Live

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

Who Should Attend

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..

Target Audiences

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..

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 !”

Course Curriculum

Chapter 1: Introduction & welcome to this course !

Lecture 1: Utilize QnA section ( Golden Oppurtunity ) !

Lecture 2: Introduction to course & its benefits !

Lecture 3: How to follow this course , must watch !

Lecture 4: Pre-requisites (Anaconda Python & Jupter install & Set-up)

Lecture 5: Introduction to Jupyter Notebook !

Chapter 2: - Project 1 : TMDB use-case -

Lecture 1: Datasets & Resources

Chapter 3: Build a Recommendation System using Average Weighted

Lecture 1: Getting a High-level Overview of data..

Lecture 2: Lets Prepare data for analysis & Model building..

Lecture 3: Getting Overview of Average_weighted_Technique

Lecture 4: Lets Recommend movies using Average_weighted_Technique

Chapter 4: Build a recommendation system using Popularity Score

Lecture 1: Lets Implement Popularity based Recommender System..

Chapter 5: Build a recommendation system using Weighted average and Popularity score

Lecture 1: Scaling & its different types !

Lecture 2: How to Normalize your Data !

Lecture 3: Lets recommend movies using Hybrid model..

Chapter 6: Build a recommendation system using Content based filtering

Lecture 1: Lets Understand about Content Based Recommendation system..

Lecture 2: Intuition Behind Bag of Words – Part 1

Lecture 3: Intuition Behind TF_IDF Part 1

Lecture 4: Intuition Behind Tf-IDF – Part 2

Lecture 5: Applying TF-IDF on our data !

Lecture 6: Applying Sigmoid kernel on data !

Lecture 7: How to design a function from scratch in real-world !

Lecture 8: Lets Build Content_based model..

Chapter 7: Build a more Advance recommendation system using Content based filtering

Lecture 1: Understand how to improve Model from business perspective !

Lecture 2: Lets perform Feature Extraction !

Lecture 3: Lets clean & prepare our data

Lecture 4: What is Meta-data & how to Create meta-data..

Lecture 5: Lets Recommend Movies..

Chapter 8: - Project 2 : Movie_lens use-case –

Lecture 1: intro

Lecture 2: Datasets & Resources

Chapter 9: Build a Recommender System using Co-relation

Lecture 1: Lets Perform Data Preparation..

Lecture 2: Applying Statistical Approaches on Data !

Lecture 3: What is Pivot_Table & how to create it ?

Lecture 4: Lets Build recommender system using Co-relation.

Chapter 10: Build a Recommender System using KNN-based Collaborative filtering..

Lecture 1: Lets explore our data..

Lecture 2: Lets Build KNN based collabarative model..

Lecture 3: How to make your Recommendations more Interactive !

Chapter 11: Bonus lesson

Lecture 1: Bonus section

Instructors

Shan Singh
Top Rated & Best-Selling Udemy Instructor , Data Scientist

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 5 votes

4 stars: 10 votes

5 stars: 24 votes

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