Learning this course, and specially after Week6, I strongly felt to have some background on statistics. I had to replay vedios multiple time to get the concept. Also the content of the course is very solid, but sometime I felt there is not much explanation of approach, sometime intermediate steps reasoning is missing. Final assignment is very well laid out touching all the models you learn in this course.

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."

This course is the perfect for one who learns the basic of machine learning and They will make sure you learn it percectly but i give it only 4 stars because the lab session was not explained by the instructor although it was liitle bit self explained by the notebook itself it would be better for us if you explain some tougher lines atleast.Overall the course was an excellent one.

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 course is outdated in some aspects. I wish it used more real world problems, and there was a deeper explanation of how the data is treated. The notebooks are used in a third party, which makes the learning experience worse, since the third party system is not automatically graded. For an introduction is ok, but it definitely needs an update on the whole course.

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.

Personalmente me hubiera gustado poder acceder al certificado de honor del curso, pero no tuve la capacidad de realizar el laboratorio debido a que necesito mejor preparación. Opino que enseñan algo muy básico de programación y mucha teoría. Entonces me siento en la capacidad de responder definiciones técnicas pero no de poner construir un código desde cero

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.

Overall course is well organized but one thing i want to suggest that as the title of course is ML with python you should have to focus on python language as well ( Introduce libraries ) instead of explaining which model works how, though course covers major topics just in sense how its works not help to write the code itself to certain model.

Great course! I love the fact that it is concise and hands-on. After you get introduced to the various algorithms, you immediately get a feel for how to solve problems using them. The level of difficulty is moderate if you have a good math and stats background, though the course does not go deep. I had great time going through this course!

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.