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

4.9
stars
2,885 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

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

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!

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1 - 25 of 494 Reviews for Unsupervised Learning, Recommenders, Reinforcement Learning

By Anupam

Nov 30, 2022

The best thing this course did for me was to remove the enigma of machine learning. This specialization is not so much about going deep into individual machine-learning algorithms and techniques as it is about exposing a student to the broad spectrum of all the different kinds of problems for which machines can be programmed to learn a solution. Once a student completes this course, they have a very good idea of the kinds of problems that can be solved by letting machines learn how to solve those problems and specific algorithms/techniques that need to be used for that particular kind of problem. A student can then research additional resources for the specific problem they have at their hand and take a deep dive into developing a working solution for their specific problem. This course enables you to start that journey by taking away the fear created by the belief that machine learning is something very challenging.

By Mi c

Jan 27, 2023

The video explanations are amazing, but the practical exercises are just frustrating. The difficulty is fine, could be more challenging actually and also require you to do more than what was explained in the videos. The annoying part are the unit tests. I almost always get a fail even though my functions return the expected output. I have compared my function to the solution and I always get the same results. I do not know how the unit tests are designed but they do not fulfill their purpose. It is frustrating that I have to write functions in a pre-dedfined way to get a pass even though my function generates exactly the same output. Even more frustrating that the explanatiosn in the notebooks do not explain why the function has to be written exactly in that way, when a more efficient way with less or even no for loops is possible. I would recommend to rather play around and write your own classes on your local machine and compare output to scikit-learn algorithms. I think you learn more that way than having to resort to copy pasting code to get a pass on the unit tests....

By ירדן א

Aug 31, 2022

week 2 and 3 left me with a feeling of a very partial understanding of the material

By Muhammad I K

May 1, 2023

So much time to spend, so much math to understand, but it's really fun to gain knowledge from this course especially machine learning intuition for me who had passion on that. Thankyou!

By Long C

Sep 23, 2022

Good general introduction but superfacial, and with too many small errors in the video contents.

By Fabrice L

Nov 11, 2022

Thanks Andrew Ng and Team!!

the courses are beautifully explained, and the lab are greatly prepared and organized!

I have wanted to follow this course for a long time, and I am very grateful that finally, I had some time to make it!!!

Special message to Andrew Ng: you make this course very special and exceptional! Indeed, your compassion and concerns to make the world a better place are refreshing in today universe. You really make the world better by sharing knowledge in a great way. I wish you all the best in your multiple endeavors :-)

Fabrice

By Talha K

Sep 10, 2022

It simply exceeded my expectations. I recommend it to whoever who is trying to learn the concepts and need tips related to industry practices, and overall wants an applied approach.

By Monojt L

Aug 18, 2022

This course is an excellent course for introductory machine learning. All of the topics are covered in great detail and It is an honor to be taught by Andrew N.G, the Great teacher.

By Yuriy G

Aug 9, 2022

Great course and very well taught by Andrew! The only problem is that now I am left with a burning desire to learn even more and start applying all this knowledge everywhere ...

By Richard G

Aug 4, 2022

Awesome specialisation. Allowed me as a beginner to get a good initial understanding of machine learning and put begin to put concepts into practice.

By Eduardo A

Jul 29, 2022

Excellent Intro to ML topics, I'm grateful to have taken this course and the explaning way for dummies of Andrew Ng. Towards ML Engineer ->

By Armin A

Oct 13, 2022

This is a great start on machine learning, And I think the great Attitude of Mr.Ng in explaining things clearly and succinctly is amazing. I only hoped that there were more and smaller programming assignments, slowly building up to the current ones we have, where we would write things from scratch. Towards the end, the programming assignments were getting complicated, but the tasks asked from the learner stayed relatively simple, and I didn't as much of deep involvement in the programming as I would have. Thank you for this great course. I learned a lot!

By Dave B

Jul 3, 2023

Enjoyed the specialization as a whole, but part 3 seemed to cover ground too quickly. I think more practice would be beneficial with more labs and perhaps there needs to be a 4th course to spread this content out or otherwise cut some out. Still 4 stars because it does give a wide overview but it felt superficial and if an overview was the goal it should have shown less theory and formula as this just made me feel like I wasn't getting it.

By Ido R

Oct 27, 2023

I feel that the part about reinforcement learning was a little confusing to me regarding the algorithms. i suggest adding an optional lab that lets the user experiment with the code and that prints out what the variables are, to get a better intuition of what it is we are actually doing. i think this was done very well throughout the specialization, but that it was lacking in the reinforcement learning part. thank you

By Svetlana C

Jun 3, 2023

While the material presented in this course was very interesting, and Andrew Ng was delightful, there was SO MUCH repetition, redundancy, and basic (yet unnecessary) math that the content could be condensed to about half the length, and anything that falls into "don't worry about it" category could be made optional. Personally, I would have preferred having to worry about it - yes, people should expect to learn and use math in these technical courses.

The quizzes were inane, truly, with the answer sometimes right there in the questions, and practice labs required minimal thought, sometimes just copying code from sample labs or lecture notes. Overall, testing materials in this course were way too dumbed down, which was disappointing.

By ALBERT T B

May 29, 2023

I recently had the privilege of enrolling in a course on Coursera, and I must say it was an extraordinary learning experience that I wholeheartedly recommend to anyone seeking quality online education. Coursera offers an extensive range of courses from renowned universities and institutions, ensuring top-notch content and expert guidance. The course I undertook exceeded all my expectations, and here's why I highly appreciate and recommend Coursera:

First and foremost, the course content was exceptional. It was thoughtfully designed, comprehensive, and covered all the essential topics in a well-structured manner. The instructors demonstrated a deep understanding of the subject matter and presented it in a clear, engaging, and accessible manner. The course materials, including video lectures, readings, and assignments, were of the highest quality, providing a rich and immersive learning experience.

One aspect that truly stood out was the interactive nature of the course. Coursera incorporates various interactive elements like quizzes, hands-on exercises, and discussion forums, fostering active participation and reinforcing understanding. The platform also offers opportunities for peer interaction, allowing students to collaborate, share insights, and learn from each other. This collaborative learning environment added a valuable dimension to the course, making it engaging and dynamic.

The support and feedback provided by the instructors and teaching assistants were exceptional. They were highly responsive, providing prompt and insightful responses to queries and concerns. The feedback on assignments and assessments was detailed, constructive, and helped me enhance my learning and skill development. The instructors' commitment to their students' success was evident throughout the course, creating a supportive and motivating learning environment.

Another notable feature of Coursera is its flexibility. The platform allows learners to study at their own pace, fitting education into their busy schedules. The course materials are available 24/7, enabling learners to access them anytime, anywhere. Additionally, Coursera offers a mobile app, making it even more convenient to learn on the go. This flexibility ensures that individuals from diverse backgrounds and geographical locations can benefit from Coursera's top-tier education.

Lastly, the completion certificates awarded by Coursera hold significant value in the professional world. These certificates are recognized and respected by employers worldwide, showcasing one's dedication, knowledge, and skills in a specific subject area. The certificates earned through Coursera courses can greatly enhance one's professional profile and open up new career opportunities.

In conclusion, I cannot praise Coursera enough for its outstanding online courses. The quality of content, interactive learning experience, exceptional support, and flexibility provided by Coursera make it a top choice for anyone seeking to expand their knowledge and skills. I wholeheartedly recommend Coursera to all lifelong learners, professionals looking to upskill, and individuals seeking high-quality education. Enroll in a course on Coursera today, and embark on an enriching learning journey that will undoubtedly shape your future success.

By Reza K

Apr 17, 2024

It was a great experience to learn directly from giant of machine learning like Andrew Ng. When the course materials are of top class and design and presentations are excellent but outsider from computer science background like me feels the implementation of any concept in the lab overwhelming because compare to what the lab already implemented and what I completed in my exercise was one or two snippets. It is very exciting to see that you have implemented a linear regression model or a recommender system or anomaly detection or land a rover on the surface of the moon but disappointing in a sense that in reality I have not planned it , design it or even do not know how it happened. Of course, the implementation provided full format and complete example if anybody wish to simulate it and replicate it but person like me need more help in the format like explaining how you have designed all those codes, how these works, and how to recruit all these relevant libraries. I know my expectations from you is more because you have already provided so many materials. So, if you please provide some explanations especially on final implementation of a topic in the lab in the form of a pdf or lecture sheet or any medium you feel more appropriate additionally for those who may want to use to for consolidation of their understanding would be very very helpful. I do know You are Stanford what is very easy to You may be very difficult for others! Finally, I want to express my sincere gratitude to my creator Allah and Coursera for its generosity for which I completed this wonderful course on "Machine Learning Specialization". Thank You.

By sumit m

Feb 24, 2024

I recently completed the "Unsupervised Learning, Recommenders, Reinforcement Learning" course by Andrew Ng, and it was an outstanding learning experience. Course Structure (5/5): The course is well-structured, with each module building upon the previous ones. The progression from unsupervised learning to recommenders and reinforcement learning is seamless, providing a comprehensive understanding of these advanced topics. Content Quality (5/5): The content is presented in a clear and concise manner. Andrew Ng's ability to explain complex concepts in a way that's accessible to learners is unparalleled. The course covers a broad range of topics, including clustering, dimensionality reduction, collaborative filtering, and Q-learning. Relevance of Projects (5/5): The hands-on projects were a highlight for me. They provided practical experience and allowed me to apply the concepts learned in real-world scenarios. The projects in the reinforcement learning section, in particular, were challenging and immensely rewarding. Instructor Engagement (5/5): Andrew Ng's passion for teaching shines through in this course. His engaging teaching style, coupled with real-world examples, kept me motivated throughout. The use of Jupyter notebooks for programming assignments made the learning process interactive and enjoyable. Community Support (5/5): The Coursera community associated with this course is vibrant and supportive. Discussion forums facilitated valuable discussions, and I appreciated the opportunity to connect with peers and share insights.

By Vaibhav M

Aug 9, 2023

Amazing courses that go into detailed explanations about the math and intuitions behind the algorithms without getting too convoluted or making things unnecessarily complicated just for the sake of it.

Prof. Andrew doesn’t just tell you the name of a function for a library (like scikit

learn or tensorflow) and give you magic numbers for parameters. You actually build the model yourself and learn what the parameters stand for and what is the purpose of those parameters and hyper-parameters.

The specialization is well divided into meaningful courses and each course is well structured so that you know exactly what you are going to learn and what key specific skills you will get after completion of a course. Because of the quizzes and practical labs, after completing a course you actually gain confidence that you can design optimized solutions for that particular set of problems.

By Ginger d R

Jan 26, 2023

The first course felt a bit easy, and I only gave it four stars. However, after finishing the second and third courses, I now understand why the hints in the labs were included and it saved me a lot of time debugging. Sometimes your answer might be the same as what is expected, but the shape or dtype might be different.

Sure, you can just copy paste the answers from the hints, but that's your loss. Same goes for the quizzes, that actually do get a bit more challenging in courses 2 & 3.

The only thing I would have liked to see would be some optional math questions, but even without those this entire specialization is hands down the best one I've taken on Coursera along with PY4E.

This specialization is definitely worth your time, and I recommend watching the CS229 lectures on youtube as you're going through it if you're looking for a bit more of a challenge!

By Yunzhang W

Oct 21, 2023

This course provides you with a solid knowledge background in machine learning with various hands-on skills and techniques in linear regression, classification, neuro networks, clustering, collaborative filtering, content based filtering and reinforcement learning. Very importantly it also teaches the logics behind each algorithm, not only teaches you how to each each tool in the packages, so better helps you understand what's going on under the hood. Andrew is a great professor that he outlines the complicated algorithms in human understandable language, so it's very beginner friendly. I really harvested a lot from the course and would recommend it to everyone who is interested in , or want to give a try in this field.

By Diego C M

Jul 29, 2022

Fully recommended course, another masterclass on ML from Andrew Ng and his team! I was able to quickly build a decent foundation on UL while enjoying the content and exercises. As in the other spezialization courses, each topic starts with the algorithm intuition before jumping into the specific math and nuances. The jupyter notebooks are excellent, I found them super effective to understand the practical implementations of K-means clustering for image compression, Gaussian distributions for anomaly detection, collaborative (and content) filtering for movie recommendations, and reinforcement learning for a virtual lunar lander 🤖 Many Thanks!

By gautam k

Apr 8, 2023

The entire course and the Specialization was very Illuminating about ML. Thanks a lot to Andrew and his wonderful team for creating these tangible course products in the form of videos, quizzes, and well documented labs!

The best experience of the entire Specialization was the Climax- watching the simulated lunar rover land on the moon surface! It was an ordinary video, and we all have watched similar videos in video games, but the realization that the 'decent descent' was made possible because of all the algorithms that had run or were running in the background literally made my jaw drop and exclaim to myself- "WOOOW !!"

By Nathan B

Aug 20, 2022

Prof Ng is a fantastic teacher! The three courses are really well structured and builds upon themselves. I expected to learn some cool things, and I sure did - some mind-blowing machine learning things! The mentors on the forum are really helpful and respond to questions will thoughtful replies, which is great.

Prof Ng is passionate about machine learning, but is also sincere & humble, and is also very mindful of the ethics of AI and how it impacts people. The course is pretty cheap, and I can tell Prof Ng really wants to pass on AI knowledge.

I'm full of admiration for Prof Ng, who is a really nice person!

By Matthias K

Nov 4, 2022

One of the best courses I joined at Coursera (including ML specialization courses 1 & 2 from Andrew). Very well organized and structured, appropriate learning rate with lots of recaps in particular for me as a newbie, strong focus on teaching the ML techniques and not on coding. It's a lot of stuff all together but the slides are a good handbook for later daily work. The only thing I missed a bit is an 'executive summary' of each week and course, resp., with the most important take-aways in one single slide. But that's just a tiny little thing. Thanks, Andrew and team for this really excellent course!