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很好

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It's fine if you have a relatively strong background in implementing "multi-step" mathematics in Python. I would not say this is for an actual beginner. Maybe not even someone who is concurrently learning Python AND this course at the same time. I'm rating the course highly for the subject matter that it presents, but I struggled IMMENSELY during the practicals (you actually code key portions of the definitions--but not the entire Jupyter notebook).

The video portion is awesome. Andrew ("Dr. Ng"?) provides an excellent "plain-english" down-to-earth explanation of the math behind the algorithms. The code, however... Well, let's just say it FEELS like one of those art-instruction jokes: "Drawing an Owl: First you start with two circles. Second, you draw the rest of the (explicative) owl."

I don't really think I have much of a furture in anything remotely involving math and programming. After this course, I honestly feel more inclined to stick to dumping data with SQL and letting the grownups slice and dice it.

Bottom line: I've been in IT for over two decades and have alot of (outdated) skills in my toolbag, but this course brought me to tears of feeling like my brain is finally starting to slip away. I just can't learn stuff the way I used to. I don't know. Maybe this will all make more sense after I've slept on it.

Great introductory course, Andrew is really talented in making everything he says crystal clear. However, I've found few minor things I don't like:

1. As someone who started this course when it was free, I can say that previous version offered much more insights and tougher assignments and harder quiz questions, it was harder overall. This version feels kinda dumbed down a bit.

2. Some (important?) things are left unrevealed, not enough attention is paid to the issue of feature selection and feature engineering (maybe some of it will be covered to extent in other specialization courses). That last assignment that included regularization in logistic regression had already given function which mapped 2d features into 27 dimensions, and someone without much math background could not really see how to map such cases by themselves. Maybe that stuff is out of scope of this course, but whole model fails if someone doesn't know how to do that input preprocessing and knowledge about algorithm then becomes irrelevant.

That being said, whole course was amazing and interactive, with really valuable content, especially for a beginner.

4.5/5 from me

Nice course that provides an introduction to supervised machine learning and teaches you how to implement the linear and logistic regression algorithms and improve their performance. Well-explained and beginner-friendly.

Easy course, but it picks up pace towards the end. It involves graded labs in Python from the second week on, for which you need to know basic Python but they also give you plenty of hints. People who have taken calculus and linear algebra classes should have no problem following the math. I would have liked more technical and math details, but that is not the purpose of the course.

The graded labs are great for beginners, but since you only have to implement bits of the algorithms (and get lots of hints), those with scientific programming experience will not benefit much. The quizzes are too easy and do not test much besides that you watched the videos.

This course is great for someone who has absolutely no knowledge of machine learning. You will leave this course feeling very confident in Linear and Logistic Regression. The only improvement that I would make to the course is to simplify the examples given in graded labs. I do not mean to make the graded labs easier, but rather to make the taught code more simplified. For example, in the last graded lab you are expected to loop through every w parameter manually and multiply against X[i] then add b after the fact. Once this is done, then you apply the result to the sigmoid function argument. The better way to teach this is to simply supply the NumPy dot product of vector W and X and add b directly into the sigmoid argument. I.E: sigmoid(np.dot(W, X[i]) + b)

While the content in this course is (mostly) rather basic, it is explained well with many examples that make it easy to absorb. It won't get you ready to do much actual ML implementation, but will set you up well to understand future material on ML. I have only two complaints with the course, both related to assessment. The quizzes and formative assessments are an absolute joke. They are all listed as requiring about 30 min, but I would be surprised if most people required more than 1 min to complete them. Conversely, some programming assignments include example "scaffolding" code in the graded cells that you are meant to complete yourself, but the scaffold bears no resemblance to the final implementation and serves only to confuse the issue.

The course has provided me with valuable insights and knowledge. However, I would like to suggest that future participants are made aware of the prerequisites. Familiarity with Python programming and a basic understanding of linear algebra are essential for a comprehensive grasp of the course material. This prerequisite information would enable learners to fully engage with and benefit from the content.

I appreciate the course's quality and believe that incorporating this prerequisite information into the course description would greatly enhance the learning journey for all participants. Thank you for considering this feedback.

Mr Andrew Ng and his current team did it again! I really liked the modules used especially the interactive ones, though it depends on how fast the system on where it is being ran through, it made understanding it relatively easy.

Though it was fairly easy to understand, the programming task is still fairly challenging. I like how the coding part is structured. It really focuses on the algorithms not just building everything from scratch.

I really hope I get into the next part of this course because with this part 1, I can already tell that I am only going to get better moving forward with this specialization.

Thank you!

I enjoyed learning the topics in this course. The material was presented effectively and the instructor Mr. Andrew Ng presented the topics in a way that was easy to understand. The quizzes weren't too difficult to complete and I like how we had optional labs. One thing I would say for improvement is to possibly have a video going over the coding assignments, just to verbally walk through things. The hints provided were extremely helpful but it would have been more effective for my learning to have audio guidance as well. Overall, this course was great and I would recommend it to others.

I recently completed the "Supervised Machine Learning: Regression and Classification" course on Coursera, and I'm thrilled with the experience. The content was well-organized and easy to understand, making complex concepts clear. The instructors were adept at simplifying intricate ideas, and real-world examples added practical value. The balance between theory and hands-on exercises, including valuable lab programs, enhanced my skills effectively. I wholeheartedly recommend this course to anyone wanting a solid understanding of regression and classification in machine learning.

Really nice and a beginner friendly introduction course, you could easily understand the content by no means is it difficult. The same goes for the optional labs and graded assignments. It tells you a lot of information on the topics. Only thing i wish to see changes around is to make the assignments and labs a bit difficult and thought provoking. I also wish there was a end-project to be submitted by us of some sort to have first hand experience ourselves without any training wheels.

Also I'm just curious the Vectorization and it's implementations in this could've been more?idk

The structure of the course is good and so is all the information presented, but you could probably pass it without understanding any of the math/code and just copy pasting from the lab + checking the hints.

Suggestions:

1) break up week 3, or at least the assignment. Week 3 took twice as long as the previous weeks combined.

2) have some additional (optional) math/coding quizzes or create a more difficult version like you have 229 & 229A at Stanford. The youtube lectures are fantastic and so are the problem sets, but none of the rigor is found here.

Love the professor by the way!!

The course is conceptually great and Andrew is an absolutely amazing instructor. Just didn't give it 5* since I felt the great theory (perhaps sometimes even a bit too deep on calculus for a "beginner" course) was not complemented by proper modern practice - I'd expect to have at least one Notebook where we'd go from A-Z in how to build a model (eg. data cleaning, preprocessing, model training with Scikit-Learn, model inference) so we could get a clear understanding of the baseline for building regression and classification models. Apart from that, I highly recommend it!

Glows: The videos were excellent. They were short, descriptive, and very clear in breaking down complex topics. I watched them at 2x speed and was still able to understand everything clearly. The labs were very helpful and ensured I actually needed to write the code myself. The quizzes built up my confidence and ensured I took away the key points from each video.

Grows: The only thing I'd add to this is to have some quiz questions in the lab too. Or instead of hints showing some of the solutions, the hints being guiding questions themselves.

Andrew is a very nice to listen to person and you know he is passionate and talented in what he explains by the fact how easy he breaks down such a complex topic into small easy-to-understand and on the point chapters. What I critique are the programming exercises. The course says it's not necessary to be able to code but it's absolutely impossible to do the coding challenges without having at least a bit of Python knowledge which was the case for me or by simply copy+paste the code from the lectures.