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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
3,684 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!

CL

Jun 30, 2024

Good pace and really well-designed for those who are total strangers to machine learning. I could follow along quite easily and looking forward to try out some of those algorithms on my own free time.

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351 - 375 of 592 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Madhav M

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Sep 26, 2024

Thanks to all the team of Andrew NG.

By hoanghero 1

•

Mar 19, 2023

Easy to understand, useful knowledge

By Tajuddin M

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Aug 18, 2022

Awesome explanation of key concepts.

By SONIBARE A

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Aug 23, 2024

Most educative. Thank you Coursera.

By Jihan K

•

Mar 26, 2023

informative, and easy to understand

By Reza A

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Oct 18, 2022

It's great course. Thank you Andrew

By Srivatsav K

•

Oct 21, 2024

Good for absolute beginners in ML.

By Wilmer R V

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Apr 19, 2024

Muy bueno, explicativo y práctico

By Sohag H (

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Jan 8, 2024

learn a lot. love from Bangladesh

By Xuesong T

•

Jan 7, 2024

very useful course! Thank Andrew!

By Boikhutso M

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Nov 29, 2023

Very well structured intro to ML,

By Jeremy L

•

Aug 6, 2022

Amazing course!

I learned a lot!

By Abhay K

•

Sep 27, 2023

You are the best teacher so far.

By Justin H

•

Mar 11, 2023

Andrew Ng. Enuff said. 👍👍👍

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

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Nov 23, 2022

Thank you Prof Andrew & team!!

By Ankit P

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Aug 28, 2022

This course is just fantastic.