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.

It was good, but I wish the "ungraded-labs" would've been graded labs and would've forced me to do some work. I did learn a lot from the content of the videos, but having to code out each week before the final project would've helped to solidify my learning. Still a good course, and the final project does ensure that you understand what you're doing.

Interesting course with information pertaining to the real world with clear examples to support the information. Actually, one of the few courses where the labs were useful in the real world and the final project wasn't extremely difficult. The videos were a lot to take in at one time but the material was presented in an informative way.

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.

This Machine Learning with Python module, so far, for me, has been the most difficult. Due to lack of understanding of Statistics. But I got help from the forum, from friends and I bought one or two books to solve my doubts. But Coursera's teaching methodology is great. And I will take other courses. Sorry my english is basic

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.

This course is great for concept of ML good enough for applying but not the best for who try to understand under the hood of ML

It's can go future if you need to know more math behind ML you need to take another course

scope of this course is too small you need more to learn about ML but this course is good to start with.

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.

I loved this course "Machine Learning with Python" because it's a good courses, easy to understand , good explanation, and this course have an application with python programming language directly. So, you can understand not just the theory but how to code the theory that have been taught using python.

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!

Loved the content, there needs to be more explanation about what Python code is necessary to complete the final project. In many cases I had to convert strings to integers and use plotting codes outside of examples. Please provide references to use that are necessary to complete the final project.

Great course for machine learning beginers. Helped me gain some general ideas about machine learning, and I'm able to conduct simple machine learning algorithm on my own after the class. Would be better if the python code has more detailed explaination, or has videos walking us through the code.

The course thoroughly teaches about all the mathematical formulas and theoretical explanation in creating and predicting of data in data models. But I think if he also teaches about various python modules used for data science too, then it would have been much easier to do the assignments.

The content is good and broad, although a little too superficial sometimes. It also provides a fair Python practice. My biggest complaint are the quizzes, that are full of bugs. Those bugs are the number one complaint in the forum, but there is very little responde from the management.