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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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551 - 575 of 592 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Tony H

Aug 15, 2024

Greate ML Specialization program. I really enjoy the teach style of Andrew Ng. I can tell he has lots of heart and really want to share everything he know. The first two course had good pace. The last course feel abit compact with lots of information being packed in. While it was missing the smaller steps optional labs to help student practice the concept on simpler problems. Instead, the course 3 only have the final labs, which is surprisingly quite difficult when being thrown into it with so many new things at once. Overall, great program! I really enjoy learning ML from Andew Ng as the foundation for DL and AI for my career change into the AI Specialization role/field. Thank you Andrew.

By Rawabi S Q A

Apr 14, 2024

The "Supervised Machine Learning: Regression and Classification, Advanced Learning Algorithms, Unsupervised Learning, Recommenders, Reinforcement Learning" course offers a comprehensive and valuable learning experience. It covers a wide range of machine learning topics, including supervised learning, advanced algorithms, unsupervised learning, recommenders, and reinforcement learning. The course provides a solid foundation in theory and practical applications, making it suitable for both beginners and experienced practitioners.

By Alejandro Í

Apr 19, 2024

Excelent course! I highly advice people who wants to get in touch with this topic to give it a try, it has all the tools to go through it even if you know few of programming, however, I would have liked more some challenges in between with real applications, or even additional labs with this real application to try it for ourselves, just my opinion, but in the overall was fun to learn all of this!

By Arnaud M

Oct 31, 2022

I would like to know more on the theory when the actions you can do depend on the state you are in. For instance, in the game of the chess, all moves are not possible all the time depending on whether a piece is on a certain square or if it is pinned... This is the case in many games including the game of Go or even soccer only the player who has the ball is able to pass it to other people.

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 Muhammad T (

Jul 17, 2024

A few things were left out, which albiet would have made the course slightly more complex, could have been added as optional / honors content for those wanting to atleast understand fully what was going on. Especially in week 3.

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 Farouk B

Sep 28, 2024

very good course but it needs lab practice , it looks like we just run the code , aand if we want to write by our self it is hard . but thank you so much

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 jaime k

Sep 1, 2024

It felt a little rushed compared to the previous 2 courses. Still really good, but it doesn't go as deep.

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 Miguel M

Jul 16, 2024

It's a good course for complete beginners, but a bit lacking in practical exercises

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.