Kursteilnehmer-Bewertung und -Feedback für Maschinelles Lernen mit Python von IBM

Would like to say that it would be of great help if we had some more practice on coding. But overall a wonderful course and helped me learn a lot. Thanks!

It was an excellent course. They covered all topics of ML. Practical session was good but need more explanation. That would be very helpful to students.

The final project is ill-structured. It is hard to grade because you need to go back and forth in the codes. It makes both doing and grading harder.

The course appears to assume good competency in Python and does not provide much training on Python. This should be highlighted to students upfront.

Course is very well structured. Some extra guidance and assistance would be nice with the Peer-graded assignment as it gets bit tricky and complex.

somewhat superficial. I think the course name should be only machine learning, not machine learning with python. There is no tutorial with python.

Learned a lot in this course, I would've liked there were more exercises throught the videos rather than some questions at the end of the section.

There were a couple of videos where the notes weren't separated with punctuations. But other than that, it was a really good course. Thank you.

This course is very well, but it doen't have model selection and stratified features selection with sklearn.model_selection.train_test_split.

It was an awesome experience to learn machine learning. The instructor has explained every algorithm in a detailed way. It was very helpful.

Good course if you have some basic knowledge of Python and data analysis. However, much of the course material had typos and small errors.

Un curso muy completo, aunque mejoraría un poco los ejercicios, que al estudiante se le diera más importancia en la resolución del programa

It's so bad when someone reviews your assignment and gives you an unfair score. But this happened a lot because of this peer review system.

Well Explained. Video lecs are very easy to understand and upto the mark...Assignments little bit need more clarification and explanation.

The Course Could have been a little better if there were more theory and more illustrations at time a disconnect was felt in the Course

Very fascinating course but exercises like final project will be more for exposure to real coding than it will be really more helpful.

Course content and presentation is really good! The only thing i would add is the tuning of hyperparamaters which makes ML what it is.

Great course but there should be videos where there is need of explanation on code as well, codes given are very good and covers basic

I really liked the course content, way of teaching and assignments.

This will definitely help a beginner in data analysis to start with

Very nice course!! You learn a lot more of the theory than the practice part, but the concepts are well explained and I learned a lot

A good course to quickly learn important aspects of ML with Python. The assignments and final exam is also very useful for learning.

quite good at the explanation and un-graded exercises.

But the knowledge could be deeper and more about parameters in Sklearn APIs.

Goodone for anyone who's a beginner in this field. But I personally suggest you to take the Data Analysis with Python course first.

Although not extremely detailed in the model optimisation part of the work, it is a very useful way to get started on applied ML.

Gives a good overview of regression and classification algorithms . It could have been expanded to other ML algorithms as well.