MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 59 lectures (3h 1m) | Size: 2.77 GB
Welcome to the best online course on Recommendation Ee.
Master recommendation systems Industry Projects using using modern recommendation techniques and methodologies
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
Master various recommendation ees 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 ee 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 ee as well as a book recommendation ee 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.
Data Analysts
Data Scientists
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