Master recommendation systems Industry Projects using using modern recommendation techniques and methodologies
What you’ll 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
- Good knowledge of Python programming
- Knowledge of Probability and Statistics concepts
- Knowledge of Machine Learning Algorithms
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.
Not only this, you will also work on three very exciting projects.
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.
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.
Who this course is for:
- Data Analysts
- Data Scientists
Created by Data Is Good Academy
Last updated 7/2021
Size: 2.85 GB