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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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176 - 200 of 592 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Kushagra L

Jun 3, 2024

Excellent learning experience. Andrew sir was exceptional throughout the course. Taught all the concepts in a brilliant way.

By Kizmaz E

Aug 14, 2022

Thanks to this course, I learned the working principle of artificial intelligence applications. Thanks a lot for everything.

By Ayushkumar P

Aug 8, 2024

Amazingly structured modules and assignments. Loved the way it was paved and off course Andrew's teaching is the best part.

By Badr T

Oct 26, 2022

Really enjoyed learning about reinforcement learning. This spiked my curiosity and interest to further explore this field.

By arjun v

Aug 14, 2023

I really enjoyed this course very much. Everything about this course was perfect especially the teaching way of Andrew Ng.

By Abhishek M

Jul 20, 2023

Great Experience! Loved to learn this stuff from coursera and also thankful to coursera for providing me with this course.

By Elízio R d A

May 18, 2023

About Reinforcement Learning I expected to see more code solving different problems, but for one week it was pretty good.

By Subhrajyoti S

Jun 29, 2024

It was really amazing all the way through! This was a great start for the fundamentals in the field of machine learning.

By Sourav D

Jan 1, 2024

This course was very helpful for me. I have learned many interesting topics and really enjoyed this course. Thank you.

By Kirandeep K

Oct 27, 2022

most ML courses mainly focus code, this courses built good foundation of whats happening in those tensorflow commands

By Roman S

Jun 10, 2024

Andrew Ng explains the content in a clearway, so that even for a person without coding skills it could be understood.

By Cameron W

Apr 10, 2023

Excellent technical introduction to unsupervised learning and the basics of reinforcement learning. Highly recommend!

By Maria d l I V

Aug 23, 2022

well documented and better structured. The exercises are very representative related to the outcomes. Congratulations

By Jimena M M

Feb 8, 2023

Great course as machine learning algorithms got much clearer to me and I will try to implement some of them at work.

By Yerunkar R R

Oct 21, 2022

The one and only course needed to the beginners to dive deep and gain knowledge in the field of Machine Learning.

By Marko N

Aug 8, 2023

Good course that offers a good starting point for unsupervised learning, recommenders and reinforcement learning.

By Mark R

Jun 4, 2024

Great learning experience! The instructor did an excellent job making complex topics understandable. Five stars!

By U.CHANDAN K .

May 15, 2023

Thank You very much for courses , Coursera and Sir Andrew for giving beautiful content in detailed way possible

By Hamad U R Q

Nov 15, 2022

Amazing course, awesome instructor.

Just a capstone project is missing. But highly recommended for ML learners.

By Vaibhav B

May 2, 2024

Incredible for begineer but my advice is to focus on doing things own your own after learning the theory part

By darius t

Jan 11, 2024

Challenging course giving a great overview as well as a deep dive into the fundamentals of machine learning

By Kerstin D

Apr 10, 2023

Fantastic course! Very well thought out and super valuable. The lectures are excellent and so are the labs!

By Arushi G

May 21, 2023

Excellent course gave me a good grasp on the topic and interested me more to learn about machine learning.

By Krishalika R

Sep 17, 2022

A good course. As a beginner to ML, this was very helpful for me to understand machine learning concepts,

By K M

Oct 3, 2024

Absolutely must-go material. (You need to find exercises on your own as it doesn't have many assignments)