5.00
(1 Rating)

Recommender Systems: Building Personalised Recommendation Systems

Categories: AI and ML
Wishlist Share
Share Course
Page Link
Share On Social Media

About Course

This course offers a comprehensive toolkit for making recommender systems operate on various platforms.

Recommender systems are now used in practically all online enterprises in some capacity including Google, Facebook, and Amazon.

Why are “recommender systems” useful and what do I mean when I use the term?

This course is going to teach you just that.

We’ll examine well-known news feed algorithms including Google PageRank, Reddit, and Hacker News. We’ll also examine the Bayesian recommendation methods that many media organisations now make use of.

However, this course covers more than just news streams.

Customers are regularly offered recommendations for goods, films, and music by businesses like Amazon, Netflix, and Spotify. These algorithms have generated additional money in the billions of dollars.

I can tell you that the information you’re about to acquire in this course is very relevant, very practical, and will have a significant influence on your business.

You can use these strategies to display the appropriate recommendations to your users at the appropriate moment whether you run an online store or simply write a blog. You can also utilise these strategies if you work for a company to win over your management and secure a rise!

See you in class.

Show More

What Will You Learn?

  • Utilising straightforward and cutting-edge algorithms, understand and implement appropriate recommendations for your users into practice.
  • Matrix factorisation and SVD in pure Numpy for big data on Spark using an AWS EC2 cluster.
  • Keras matrix factorisation.
  • Keras's autoencoder.
  • Deep neural networks, and residual networks.
  • Restricted Boltzmann Machine using TensorFlow.

Course Content

Getting Started

  • Introduction and course overview.
    06:41

Environment Setup

Python Coding For Beginners (Extra Help)

Some Basic Recommendation Systems

Collaborative Filtering

Deep Learning and Matrix Factorisation

Using Restricted Boltzmann Machines for Collaborative Filtering

Using Spark Cluster on AWS / EC2 for Big Data Matrix Factorisation

Bayesian Ranking

Conclusion

Course Completion Quiz

Student Ratings & Reviews

5.0
Total 1 Rating
5
1 Rating
4
0 Rating
3
0 Rating
2
0 Rating
1
0 Rating
3 years ago
Excellent starting point for experts and also provides valuable insights.
Scroll to Top