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Back to Unsupervised Learning, Recommenders, Reinforcement Learning

Learner Reviews & Feedback for Unsupervised Learning, Recommenders, Reinforcement Learning by DeepLearning.AI

4.9
stars
5,230 ratings

About the Course

In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

Top reviews

HZ

Jul 21, 2024

Got insights to what Recommender systems are and how it recommends user based on their usage and got to know how Reinforcement learning works and Successfully landed the lunar lander.

ML

Aug 17, 2022

This course is an excellent course for introductory machine learning. All of the topics are covered in great detail and It is an honor to be taught by Andrew N.G, the Great teacher.

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476 - 500 of 806 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Srivatsav K

•

Oct 21, 2024

Good for absolute beginners in ML.

By Amos P

•

Jan 11, 2025

Well structured and easy to learn

By Wilmer R V

•

Apr 19, 2024

Muy bueno, explicativo y práctico

By Sohag H (

•

Jan 8, 2024

learn a lot. love from Bangladesh

By Xuesong T

•

Jan 7, 2024

very useful course! Thank Andrew!

By Boikhutso M

•

Nov 29, 2023

Very well structured intro to ML,

By Jeremy L

•

Aug 6, 2022

Amazing course!

I learned a lot!

By Nguyễn N M

•

Sep 18, 2025

a very good course for beginners

By Sikiru Y

•

Jun 6, 2025

I found it profoundly impactful.

By Mathivathani S

•

Apr 25, 2025

Excellent course, Thanks Andrew!

By Jalal U D B

•

Jan 22, 2025

best course for machine learning

By Abhay K

•

Sep 27, 2023

You are the best teacher so far.

By Justin H

•

Mar 11, 2023

Andrew Ng. Enuff said. 👍👍👍

By Joshua M

•

May 29, 2025

Great Course. Sign up. Learn.

By Jorge A N

•

Nov 16, 2024

Exceptional course. Very clear.

By Gloria L

•

May 4, 2024

I enjoyed taking this course :)

By Rolando R Z C

•

Apr 25, 2024

Very carefully designed course.

By Mirro S

•

Feb 23, 2024

nothing but absolutely AMAZING!

By Arjun V

•

Dec 23, 2023

Learn many things, Thanks a lot

By Muhammad F R

•

May 29, 2023

This course was really helpful.

By Minh D V

•

Jan 19, 2023

Very informative and well-paced

By Mati V

•

Nov 3, 2024

Nothing more than exceptional!

By Subodha G

•

Nov 26, 2023

Highly recommend for beginners

By Nguyen H

•

Apr 6, 2023

Great course and great teacher

By Rishi D

•

Nov 23, 2022

Thank you Prof Andrew & team!!