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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
3,894 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

JT

Jun 7, 2024

Recommender Systems, Reinforcement Learning culminating in teaching a simulated Lunar Lander to land itself! I bet SpaceX something similar for the 'real' starship landing; it's much more complicated!

RD

Sep 16, 2022

great introduction to machine learning. I tried to self study before but it didn't work and thanks to this course I did understand now a bunch of things I cant wrap up my head with. Thank you for this

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376 - 400 of 620 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Jeremy L

•

Aug 6, 2022

Amazing course!

I learned a lot!

By Abhay K

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Sep 27, 2023

You are the best teacher so far.

By Justin H

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Mar 11, 2023

Andrew Ng. Enuff said. 👍👍👍

By Jorge A N

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Nov 16, 2024

Exceptional course. Very clear.

By Gloria L

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May 4, 2024

I enjoyed taking this course :)

By Rolando R Z C

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Apr 25, 2024

Very carefully designed course.

By Mirro S

•

Feb 23, 2024

nothing but absolutely AMAZING!

By Arjun V

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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!!

By Ankit P

•

Aug 28, 2022

This course is just fantastic.

By AKULA P R

•

Oct 22, 2024

very good learning experience

By Crhis

•

Jul 17, 2024

This is just perfect teaching

By Ali A

•

Jan 26, 2024

Best course i ever take in ML

By David R

•

Oct 7, 2023

Andrew is a wonderful teacher

By abdelrahman a

•

Sep 6, 2023

Thanks a lot for your efforts

By Huajie C

•

Feb 13, 2023

Great course, love Andrew Ng.

By Yongli L

•

Jan 4, 2024

Absolutely love this course.

By Harsh J

•

Oct 18, 2023

best ml course in the market

By Tanu J

•

Oct 11, 2023

Specific, simple and through

By Ananda B

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Jul 3, 2023

A lot of new topics to cover