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....

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.

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

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!

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

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.

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

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

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.

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.

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 ...

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

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 ->

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!

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.

One the positive side, the course materials are well explained and up to date. As a negative point, the practical labs are not really challenging, with only a few lines of code to be written, the rest is already given - this leaves up to the student to take the time to go through the rest of the code to really understand the methodology.

weakest of the 3 courses

hardly any optional labs, the graded practice labs were too easy

More labs would have been nice especially for non initiated student in Python

The course is pretty dragged and uninteresting excepting for week 3.

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.

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.

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.

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.

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!

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.