Avis et commentaires pour l'étudiant pour Apprentissage mechanique pratique par Université Johns-Hopkins

Insightful and practical ! One of the best so far.

Good course, I miss more practice exercise because theory is always welcome but when we are capable to understand is doing real practice.

Very informative and the project was fun to accomplish.

Great course with great materials. Easy to understand and to learn.

The professor has a very clear lecture, brief and persistent comparing to others. I just love this course .

insightfull

Excellent lectures.

Great class! I wish you would do a little more explanation about what methods are best for which scenarios. If you did in fact explain that and it went over my head or I missed it, I apologize. Great class that I learned a lot from.

Good introduction to machine learning. Provides pretty comprehensive coverage of major algorithms and approaches.

elementary course and not too much work

It's good that they teach you basics of machine learing in R (caret package), but it's very introductory course. I definetly recommend this course to beginner, but I also recommend taking more courses on this topic (Andrew Ng's for example).

Very good explanation! Trying to do the examples help me understand more plus the explanation which is not on the slide helps a lot. Thank you

So far, one of the most fulfilling courses in the Data Science specialization!

A fast-paced course that got me going in building models and understanding the pitfalls. I felt the directions for the final project were somewhat poorly worded and vague (and calling one of the files test when it was not to be used for testing the model was initially confusing), but overall it was good. I would have liked to have seen the final project uploaded as a secure file as has been done in other courses, and Github was a poor platform for viewing html files. Additionally, the question about out of sample error caused many people problems in the projects as they confused it with with Accuracy, yet it was weighted heavily in the rubric: I'd like the instructors to review the materials how that material is presented in terms of models. I got 100%, but as always you have to pay very close attention to the rubric.

As always with this specialization, you are really just given a taste and there is no way you can fully explore all the material and references presented., but it is enough to get you going and wanting to come back and explore the material more.

Great intro to machine learning. Covers the basics to allow you to being using ML concepts on your own.

It was a great overview of the fascinating word of ML.

Excellent introduction to machine learning. Great examples and detailed explanations, as usual

Excellent course

I liked everything abt it

excellent course and very beneficial

It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.

Include more swirl practice problems.

need eva on my project

Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice

I've been working my way through the whole track, and this was by far the most complex material--but it was easy to understand because the videos were so clear.

I do have one bone to pick, though: the quiz material relies on very old packages. Again and again I had to finegle something so I could answer a quiz question. That makes you guys look like you are lazily sitting back collecting money but not really doing your job as far as teaching goes. It's time for an update. How hard is it to run your quizzes on updated packages and offer answers that are current?

Aside from that, I find that you explain material very clearly and you are my first choice for picking up a new data science skill.