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

4.9
stars
3,022 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

AV

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Amazing content, perfectly curated topics with hands-on labs, although Assignments and labs could be more challenging based on certain level students who already have programming backgrounds.

JT

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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!

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476 - 500 of 510 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Rithvik M

Apr 26, 2024

I think that this course goes very in-depth into how machine learning works. On top of that, he talks about the code and real world applications so transitioning this over to work or school is very easy. The practice labs have simulations and graphs to show exactly what your code is doing. Overall, just a very informative and relevant course.

By Hou H I

Feb 29, 2024

Very well on introduction the basic concept of the topics, but some of the function are not visible enough for us to understand (such as the update boolean in the assessment of reinforcement learning)

By Aquib V

Mar 1, 2024

Amazing content, perfectly curated topics with hands-on labs, although Assignments and labs could be more challenging based on certain level students who already have programming backgrounds.

By Hunain A

Sep 26, 2022

The content was details, explained thoroughly and understandable. But, when it came to implementation, few more labs similar to the structure of previous course could have improved it more.

By johann s

Apr 15, 2023

The part on RL is obviously more difficult but gives a good understanding of the foundations and principles.

Overall an other great course taught by Andrew NG!

By Nguyễn Đ D

Mar 29, 2024

Lack of hands-on experience in coding (i.e. the implementation of the algorithm). Need more detail explanation and careful guidance throughout the notebook.

By José L F G

Jan 7, 2023

Very instructive and interesting. There were some videos were the slides were very cluttered with calculations (e.g., the derivative optional video).

By Vikas S

Mar 18, 2024

The lab assignments are feel happy in nature. They should force the learner to write more than just the code for hidden layer selection. Thanks!

By Arsam A

Jul 12, 2024

the content and theory are very good in the course, Andrew is an amazing instructor I just wish there were more coding exercises.

By Santosh R

Jul 1, 2024

All the contents were excellent except reinforcement learning. The videos seems very less and not very understandable.

By Raghavendra N

Aug 2, 2022

Great course on understanding key machine learning techniques without getting too deep into the mathematics.

By Marc A

Jun 5, 2024

The labs are not very challenging, maybe some more coding would help to understand more material.

By Derk v G

Dec 15, 2022

Nice content, the speed of speaking was a bit slow. Fortunately I could watch at 2x the speed.

By Alejandro S

Jul 9, 2023

The course needs more application excersices and no just the theory of the concepts

By Yash B

Dec 29, 2022

great course but practice labs weren't challenging nor tested material well

By Janardhan P P

Jan 24, 2024

few topics were little complicated , specially reinforcement algorithm

By Mario

Feb 16, 2023

great course and content

The reinforcement learning part can be better.

By Mayank D

Dec 10, 2023

the practice assignments were very disturbing and took lot of time

By Wassim R

May 31, 2023

Thank you.

I suggest adding more optional labs before practie labs.

By vaibhav v

Mar 30, 2024

I think the exercises could be a bit more challenging

By Nathan H

Oct 16, 2023

great foundational course with the fundamental math

By Abhinav P

Jan 7, 2024

wish it were more implementation focused

By Vivek J

Jul 2, 2023

Best for begineers and little advance

By Kateryna K

Mar 15, 2023

I would do more challenging labs

By Jimmy R

Dec 6, 2023

Great stuff, learned a lot.