Mostly a very nice course introducing the basic ideas behind many standard techniques together with the basics on how to implement them. Gives a good start to learn ML further. One star lost from the fact that some of the quizzes are badly designed -- multiple choice questions with slightly ambiguous answer possibilities where you get no partial credit nor any feedback on what went wrong. I still have no idea why some answers were right or wrong as I just had to try too many different quesses to get a passing grade.

Great video material and clear structure. I also like the JupyterLab integration. The exercise notebooks need some cleaning up though: Lot's of grammatical errors, inconsistent coding conventions (snake_case vs camelCase), poor variable naming, programming mistakes resulting in incorrect accuracy scores, outdated libraries (there are provided functions for rendering confusion matrix and plotting decision trees in sklearn, which could be used). It shows that the notebooks have not been created by Python experts.

I really enjoy this course. It teaches me a lot of basic machine learning model, method and data analyzing technique. However, I still recommend that it should have coding assignment for every week exercise. It is because learning from video is simple but hard to do implementation. The best way to learn data analysis is to implement or do the real stuff by ourselves. It is necessary to put an assignment to force every learner try and error. This is my opinion for this course.

The rubric for the last assignment was too arbitrary. People with little to no machine learning experience will assume that submissions have to be cookie-cutter copies of previous labs in order to achieve 100%. I would put force students to put random seed on models in order to achieve similar results to achieve more homogeneity and therefore an easier way to grade. Perhaps you could put a section at the end that allows for further parameter tuning if the student so desires.

This course is great for begginers. Basic theory of simpliest algorithms and techniques is given in really simple way. I enjoyed to listen to videos. However, there is not enough practice coding. Final project was the only challenging task during the course. Another drawback - misprints. In addition, goals of the final project were not clear as for me. To sum up, this course is good just for basic theory review.

The course is good but sometimes the exercise texts are not very clear and some of the lessons are very straightforward, leaving many doubts. The course should have a larger series of exercises and an automatic correction system that facilitates the review of the exercises. In addition, it would be interesting to have a module on how to use IBMDB2 without the online platform, but through Jupyter on the computer.

The material is comprehensive covering almost all of the popular models. Unfortunately, the peer-graded assignment only covers classification models so the practice on clustering is lacking. For real world problems, this module is probably the most useful so it would be beneficial to include more practice on clustering for examples. Overall, it is an interesting course with lots of new ideas for beginners.

I particularly enjoyed this course. It is easy to understand it even with a basic knowledge of Python. Lab exercises are well-writen and very helpful for the completion of the course. I think it's a great introduction to programming using SciKit Learn. Personally, I would have liked to learn a bit more about the mathematical background of the algorithms but maybe this is out of the scope of the course.

Pretty good course, but you REALLY need to put in your own time to get anything out of it. You really could probably complete this course by just copy-pasting into the assignments. I wish there was slightly less hand-holding throughout the course and more having to do more work on your own with proper guidance, rather than just "here's a video" then "here's a notebook. Run it and see what happens."

I enjoyed this course and felt like I learned a lot! The reason why I'm not giving 5 stars is because some of the assessments need work -- instructions and wording on questions were either confusing or contradictory (for example, on the final project you are asked to find the best k value for 4 different types of ML algorithms even though only one of them has "k value" defined).

The course was good, generally. Instructors as well. I had used IBM Watson and Jupiter Notebooks which was really usefull. But it would be great if you add more real world examples for algorithms use cases. Errors in the presentations and in the Jupyter workbooks, which were mentioned years before, and still have not been fixed are really unprofessional. Anyway, thank you.

This course is relatively good. If you are looking for a introduction to machine learning this is the course for you as it covers most of the methods over a short period of time. The downfall of this is that the algorithms are not covers in detain in particular their optimization and limitations.

Also the exercise are done on the IBM development platform which is garbage.

The material was good but the servers for the ungraded projects could use some work. I had connectivity issues with each project I tried to attempt and even now when I tried to reference the material to improve my models I could not access them. Other than that I thought that this course was very informative and helped me become an overall better programmer.

This course is best under to understand the theory part of machine learning and this will give ou understanding about the python library ScikitLearn , logistic regression and machine leaarning wth python . But there is some missing i found while study this course is programming (coding) part which is not given by teacher.

Thanks !

This is my favorite course in The Data Science Professional Certificate. Using real-world examples we implemented several ML models using scikit learn and python. There is also some exposure to numpy. This is a good course and overall provides applied data science methods with a comparison of common methods for classification.

Valuable material and well organized. There are many gaps in the explanations though. In the sample notebooks, there is a LOT of code that is not explained, so I have to Google the code or skip over it. The final tests a skill (transferring a machine learning model to an separate database) which was never taught or addressed.

I think the course structure is great and provides a good overview of the various machine learning algorithms. In my opinion the coding excercises could dig a little deeper into the subject matter and sometimes a little more detail on the maths behind the algorithms would be beneficial. Overall it is a good introduction.

Although a great course some of the materials are outdated. Some codes did not work without importing proper libraries/modules, needed time to figure out. The Watson Studio/IBM cloud looks different now than in the video in the course, so, it takes more time to figure it out.

In summary: Great, but needs an update.

I had an amazing learning experience in this course. Although, i had challenges understanding some parts of the code, i found that i was able to scale through the capstone project without much stress. To further improve on the experience, it will be nice if some strange codes are properly explained and documented.

Great course!

Just a short notice about the final exercise. It would be helpful to guide the students a bit further. I didn't know what to do with so many "blank lines" to fill in. In my opinion, you should whether explain what to do in each line or just leave a "big blank line" where we can write our scripts.

I liked the course but felt that a beginner would not be able to cope up with the speed. However, if someone already has a basic knowledge of data analysis using python, this course would be perfect. I especially liked how each algorithm was explained in detail, how it works and what parameters effects them.

The courses prior to this course in IBM Data Science for Professional were simpler, and the codes were easier to understand. Some of the codes in the labs session for this course were difficult to understand. However, overall the course was a lot more effective and more in depth.

Thank you for this service.

Creo que este curso es muy avanzado para mi. Me gustó porque el instructor explicó muy bien de donde viene y como funciona las distintas metodologias, pero a nivel de practica algunas cosas no fueron explicadas y en el projecto final fue un poco confuso iniciar aunque realmente era aplicar las practicas.

Material was great. Tutorial videos were great. The only improvement I would expect is on the Labs. Not only the lab environment was slow but there were certain errors in the questionnaire for final assignment for quite some time which no one seems to correct. If this is rectified, its a great course.

It's a good course to have some idea about ML case. A good part of the course is that, it has some brief expenations people to let them work further. Maybe not a learning course but a very good start. I do appreciate the instructors capacity to give lecture and summirize. Thank's for all the afford!