The parts on regression are previously covered in other courses that are part of the IBM Data Science professional certificate. Overall, there is a lot of information covered in this course but it feels rushed and done in not enough depth. It is an ok course for an overview of machine learning methods, but sits in a weird spot of trying to be too broad while being detailed, but too shallow for a rigorous study of each method.

I understand that this is a higher level course, so it may be designed in such a way to require learners to take bigger leaps, but I did not feel the explanations of what was required on the final were very clear, and once I graded other people's finals, it was clear that it was not clear for almost anyone.

Not a terrible course, the material and the topics were good, but better explanations are needed, I think.

On overall IBM data science professional certificate track: Pros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

There needs to be significant proofreading done on this course by a native English speaker. Additionally, the functionality of IBM tools (Watson Studio, Skills Lab, etc.) leaves quite a lot to be desired. The free tier services and trial accounts often do not work and, thus, you are stuck upgrading to a pay-as-you-go account to finish. The final course project requires some untaught ml skills.

While the course does give a pretty good introduction to the concepts behind most machine learning algorithms and enables us to realize how ML works, the problem lies in the code. None of the code is explained in detail, so the course is extremely theoretical. It basically tells you to copy the code for your own use with small edits but does not explain how to write the code in the first place.

A good course to learn know-how of Machine Learning using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.

Good introduction, but not complete.

The course does well in introducing Machine Learning, and covers a good range of classification algorithms. However I feel doesn't go the full length. The labs very briefly cover implementation but I find that it falls short. There's a lack of polish in the material, while typos are minor, the labs are can be jarring and hard to follow.

While the videos are very intuitive and helpful, the assignments are lacking in providing machine learning coding skills. Many labs are simply reading others' code, which may be a bit helpful but I doubt if students can come up with the code for other similar problems. I expected better technical skills coming out of hands-on labs and projects from this course.

The course gives a useful insight into machine learning algorithms and model creation using the python library sklearn. I liked the content, even though a little bit more mathematical background would have been nice. The exercises were good, but there was much of it already written in advance for us to use, so I didn't learn as much as I would have liked to.

The course presents a useful overview of basic machine learning techniques without going into mathematical detail. The weekly test questions can be improved to assess the non-qualitative aspects of the topics covered. Using scikit-learn is well illustrated by labs using Jupyter Notebooks. There is plenty of room to update and improve the contents.

Overall, I found this course to be enriching. However, there were more than a few errors and unclear directions in instructions for the final assignment. I hope that the course team is/will update the assignment instructions so that they are in line with current version of the required libraries and will make the instructions more precise.

Very much enjoyed the course and am thankful for the great content, however the peer-grading process created some unnecessary headaches. On how to improve this I posted in the forum here: https://www.coursera.org/learn/machine-learning-with-python/discussions/weeks/6/threads/JmWRnLUqSfClkZy1Kinw6Q

Thank you nonetheless for a great course!

This course provides a great introduction to machine learning. The first 3 weeks are in detail and well explained. The 4th and 5th weeks are not explained as expected. The Labs helped a lot in understanding the practical implementations of the algorithms. However, there should be a little explanation of what is going on in the code.

Material covered is substantial.

You get a good overview of machine learning and some algorithms that are used. (Not always in depth.)

My biggest problem with the module is with the end assigment which is not clear in my opinion (and of some fellow students in the forums who also passed this module) This unclarity is not addressed.

Me gustaron mucho los temas del curso! Los videos son buenísimos para entender la teoría; sin embargo, en los laboratorios no está documentado el código y hay muchísimas funciones nuevas que son usadas y no hay ninguna aclaración de cómo se usan o para qué se usan. Sólo en un laboratorio encontré todo documentado y explicado.

This was by far the most time intensive course, not because the topic is so difficult but because the intructions for the final assignment are so vague and unclear. Otherwise the theory sessions were good. But whole structure of final assignment from definition all the way through marking is not clear and VERY time consuming.

I doubt anybody would learn machine learning from this course. It is more of a refresher course than anything significant. There's a lot of information in the videos, but the notebooks are much more difficult. Many of the visualization aspects of the modeling are already completed in the notebooks with little explanation.

The course content is good. But final assignment needs updation. e.g jaccard_similarity_score is

deprecated. It needs to be charged in the notebook. There are less experienced candidates which get this wrong. And it is unfair. They are not expected to know that it is deprecated(That is not the course criteria)

The coding part should be explained as well. The autofilled code makes the learner lethargic and lazy to code himself. I;ve faced this difficulty and I cannot certify that I am 100% sure of what code I've learnt. Please take proper steps in order to teach CODING as well and not only theory.

the level of the course was lower than I thought it will be.. especially comparing to the final assignment.

nevertheless it did give me a strong basic for most of the materials at least to the level I will be able to explain each topic to one who doesn't know nothing about machine learning.

This course is a great introduction for people who have a background in Python and mathematics, but from a personal perspective, it should pay more attention to the details of the machine learning algorithms and special cases and do more practice using harder, more inconsistent use cases.

Some code (particularly relating to density based clustering) is outdated and needs updating. Also, quizzes with multiple-choice answers such as "none of the above" or "all of the above" need to be pinned to the last choice, rather than randomly placed, which creates lots of confusion!

Very demanding, that is great!!

It is not updated, therefore many instructions are incorrect and instead of 1 hour, one can use 2 or 3 days.

The system Watson Studio is not working all the time. I lost many lines in my final project because it stoped before I could save the changes

The only OK course in the specialization. I found that the intuition/concept behind various algorithms was explained quite well. The mathematics, on the other hand, were basically skipped. And as always, the assignments are sadly pretty much "copy and paste from the examples".

Good course and quite relevant. However, the project was not gearing up for the final Capstone project. I did not feel the skills I gained from this course set me up to succeed with my Capstone project. I felt like I was still in the dark running any kind of machine learning