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Master recommendation systems Industry Projects using using modern recommendation techniques and methodologies

What you will learn

Learn about the different types of Recommender Systems

Learn about Content based recommendation system

Learn about Collaborative based filtering

Learn about Singular Value Decomposition

Learn recommending movies, books using the recommendation system

Learn about Surprise Library for recommendation systems

Learn how to use Correlation for Recommending similar Movies and Genres.

Learn how to use different Techniques used for Imputing Missing Values.

Quizzes and exercises

Open Jobs Analyzer and Recommendation System

Description

Welcome to the best online course on Recommendation Engine.

Master various recommendation engines including Content based filtering, collaborative filtering, Singular value decomposition.

Recommender systems aim to predict users’ interests and recommend product items that quite likely are interesting for them.

A recommendation engine is a type of data filtering tool using machine learning algorithms to recommend the most relevant items to a particular user or customer.

It operates on the principle of finding patterns in consumer behavior data, which can be collected implicitly or explicitly.

This course gives you a thorough understanding of the Recommendation systems.

In this course, you will cover

  • Use cases of recommender systems.

  • Content-based filtering.

  • Filtering movies based on genres.

  • User-based collaborative filtering.

  • Item-based collaborative filtering.

  • Singular value decomposition using Surprise library.

  • and much, much more!

Not only this, you will also work on three very exciting projects.

I am always available to answer your questions and help you along your data science journey. See you in class!


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You will learn to create a movie recommendation engine as well as a book recommendation engine and Open job analyzer system.

It will be fun working on such exciting projects.

You will see how easy it is to recommend new books or movies based on the user’s past preferences.

Instructor Support – Quick Instructor Support for any queries.

I’m looking forward to see you in the course!

I guarantee you will love this course.

All the resources used in this course will be shared with you.

Don’t wait and Enroll now.

English
language

Content

Introduction to Recommendation System

Introduction to Recommender systems
What are it’s Use Cases
Types of Recommender Systems
Evaluating Recommender Systems

Content Based Filtering

Introduction to Content Based Filtering
Preprocessing the Data for Content Based Filtering
Filtering Movies Based on Genres
Introduction to Transactional Encoder
Recommending Similar Movies to Watch
Quiz on Content Based Filtering
Quiz Solution

Collaborative Based Filtering

Introduction to Collaborative Filtering
Preprocessing the Data for Collaborative Filtering
Implementation of User Based Collaborative Filtering
Interpreting the Results obtained from User Based Filtering
Implementation of Item Based Collaborative Filtering
Quiz on Collaborative Based Filtering
Quiz Solution

Singular Value Decomposition

Introduction to SVD
Implementing SVD using Surprise
Interpreting Results Obtained from SVD
Comparing Content, and Collaborative Based Filtering
Quiz on Singular Value Decomposition
Quiz Solution

Case Studies from Giants

Case Study for Netflix
Case Study for Youtube

Movie Recommender Systems

Setting up the Environment
Taking a Deep Dive into the Dataset
Understanding the Problem Statement
Missing Values Imputation
Top 10 Profitable Movies
Manipulating the Duration and Language Column
Extracting the Movie Genres
Top 10 Most Popular Movies on Social Media
Analyzing Which Genre is Most Bankable?
Loss and Profit Analysis on English and Foreign Movies
Gross Comparison of Long and Short Movies
Association between IMDB Rating and Duration
Comparing Critically acclaimed Actors
Top Movies based on Gross, and IMDB
Recommending Movies based on Languages and Actors
Recommending Similar Genres and Movies
Key Takeaways from this Project
Quiz on Movie Recommender Systems

Open Jobs Analyzer and Recommendation System

Understanding the Problem Statement
Setting up the Environment
Taking a Deep Dive into the Job Dataset
Analyzing the Job Metrices
Finding Important Metrics for Salary
Taking a Deep Dive at the Naukri Dataset
Finding Locations with Highest Job Vacancies
Analyzing the Experience required for Jobs
Most Demanded Degrees for Jobs
Analyzing the Industries with highest no. of Jobs
Analyzing the Top Skills required for Jobs
Cleaning the Rest of the Dataset
Gathering Vital Information from the Dataset
Making a Function to Search for Jobs
Understanding Relation between Industries and Education
Key Takeaways and Findings from the Project
Quiz on Open Jobs Analyzer and Recommendation System

Outro Section

Conclusion
How to Get Your Certificate of Completion

Bonus Section

Bonus Lecture