學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

Course materials are very systematic and instructive, and the professor teaches very clearly. I like this course and recommend it.

Practical class to learn well-known models and scikit-learn. The practice tests are great to help you move from theory to practice.

Good Course. Not for beginners starting with Machine Learning. Intermediate level. Prior knowledge of python libraries would help.

The exercices and evaluations are more complex than in the previous courses in this short program, but also much more instructive.

It was really an excellent well designed course, I gained valuable information that I will use as a business analytics in future.

Very good course. Not very deep, but definitively very wide and appropriate for an overview course of machine learning in python.

I appreciate so much this course even it was so dense and slitly short. It would be useful to extend it over several weeks again.

Excellent course to understand and learn about how to work with available classifiers in scikit learn. Thanks for this course :)

it is a pleasure to learn about machine learning course. I can remind and study again about the main things in machine learning.

Very solid course. Covers so many key machine learning concepts in a short period of time. Week 2 is intense - but awesome!

Useful for understanding the application part of ML whereas Andrew Ng's course gives a more in-depth understanding of the topics

The course was very well designed. The pace of the lectures are perfect unlike other course when the instructor moves very fast.

Broad coverage.

Good project assignment.

Staff not answering questions very promptly at discussion forum.

Cannot download slides.

The course material and Professor Kevyn Collins-Thompson is awesome. A person who's seeking to learn ML should try this course.

World class course.

Covers a lot of core machine learning subjects in an accessible way with a practical focus in Python.

Thanks!

Nice pragmatic approach how to apply machine learning. Compelling examples, datasets and useful tips how to visualise features.

For applied machine learning, outstanding. It could be improved with bit more theory, which gives more insight to the concept.

Very good course, for people who want to apply Machine Learning without worrying too much about the theoretical aspects of it.

Very well designed courses! There are many materials to go in depth even if you have done Python Machine Learning in the past.

Excellent course! It teaches you the basics of Machine Leaning, and merges the knowledge already acquired in the first module

Had all the basics of Machine Learning algorithms, but they need to update the syllabus with some trending boosting concepts

Great! loved the final project, which is a machine learning project that you can actually put on your resume and talk about!

I am very satisfied with this course. I learnt a lot of techniques from the course that I can apply in my research project.

very good course for intermediate level learners .learned a lot in such a short time.thanks to prof.Kevyn Collins-Thompson.