A good and detailed course on machine learning.A very good course for beginners to understand machine learning.

The final assignment was very insightful and helpful.

Loved the way of teaching, the course was worthy and I boosted my skills but the content was not too vast which I was expecting , very easy to complete the course

Good theoretical and mathematical explanation . But the python programming was not explained . We should learn them by our own by reading in the tools in coursera

3.5 stars. Serves its purpose. Skims over much of the important mathematical details. No coding demonstrations other than what you see in the notebooks provided.

The course offers short videos that explains machine learning concisely is quite efficient, and also provides a platform to learners to practice in lab sections.

I found the quality of teaching to be of good standard, however setting up IBM Watson studio and creating a project was very inconvenient and took a lot of time.

Well designed course for clear understanding of ML algorithms. Practical Labs gives you hands on experience to try out various algorithms. A very helpful course.

Good part: This course provide a lot of concrete designed materials.

Not very good part: the content of videos are too simple, without any theoretical analysis.

This course is great but one needs more time to read through other information sources. An in-depth understanding is critical to get the bigger picture of use.

Pros. Good for running code in Jupiter Notebook environment.

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Cons. Language Support only English (others are few), and Screen sub overlays presentation text.

Excellent course . Concepts of machine learning algorithm were explained clearly and easily.

The interface with IBM notebook could have been much smoother ,

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.

overall the course is very good and covered the topics in ML, but the coding examples should be explained in a videos to clarify all points in details.

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.

I am new to Machine Learning , as anew student I find the course simple to understand. I need to work on my lab skills which I will finish slowly.

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.

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.

Useful overall. However some of the topics and concepts were not explained in good detail. Also there is some ambiguity in the test questions.

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 for an introduction to machine learning. the material could have been a lot more deeper on the various algorithms and when it is used.

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