Bewertung und Feedback des Lernenden für Maschinelles Lernen mit Python von IBM

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(snake_case vs camelCase), poor variable naming, programming mistakes resulting in incorrect accuracy scores, outdated libraries (there are provided functions for rendering confusion matrix and plotting decision trees in sklearn, which could be used).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.

Good grounding into machine learning techniques with python. Bit slow at times and would like to have more emphasis on the application of techniques on real data sets e.g. dataset requirements and effectiveness of algorithms on datasets of varying size, and how to avoid overfitting etc. Also it appears as though the requirement to sign up to IBM Watson Studio is not actually required although you are heavily led that way.

The course is well done and covers many of the basic ML concepts. The reason I gave it 4 instead of 5 stars is because the final assignment asks you to do something that wasn't covered in the course, and it's not very clear either what they're asking you to do. I was able to figure it out, but it was a bit frustrating at the time (especially since I got all the way to the end and realized I had to do something different).

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.

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.

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.