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

Great course to gain basic ML skills and start building first models. Excellent starting point. Combined with Andrew Ng`s course on Machine Learning it`s great foundation for futher development as AI specialist.

Machine Learning Fundamentals are taught in concise and easy to understand manner. Some of the ML algorithms such as Kernelized SVM have been explained brilliantly. Thanks for putting up this wonderful course.

It is a very practical course if you have learned the Andrew Ng's Machine Learning course. It is much much more practical and I have gained a lot from it. I really wish I could learn it soon. Thanks very much.

Learned a lot from this course, very informative. One thing have to say that its not for absolute beginners, this course required prior knowledge of ml and python which will ease completion of course. Thanks!

Maybe this would be difficult to implement in a time-constrained course, but it would be nice to have more insight into inner workings of various algorithms... Because otherwise this course resembles botanics.

I loved the course. The explanations are simple and full of day to day life examples. The final assignment was based on a real world problem, showing how the concepts can be applied not just in a play dataset

From this course you will learn direct application of Machine Learning using python. You can dive into the world of machine learning. Ipython notebooks used are really helpful. Learned a lot from this course.

This course made me learn a lot machine learning techniques by experimenting them myself. It's more than just watching the class videos and running the notebook. You need to be ready to get your hands dirty!

It is definitely the best-organized, best-paced, most-worked-on course in this specialization, and from the MOOCs I have ever taken. Strongly recommend for your knowledge and career advance. Great professor!

Excellent course. I liked the projects in this course to recap the theories that I learned in the lecture and examine the new knowledge that I learned by myself with reading python library documents online.

Well-organized and useful course. The quizzes and programming assignments held at the end of each week make it practical and help to develop one’s problem-solving skills on a real-world dataset. Thank you.

Good class, and it's very nice to have the "applied" machine learning angle (as opposed to focusing on the mathematical / theoretical underpinnings, which are only important at a much later point in time)

Excellent introductory course to machine learning using python. It covers the basics for the popular supervised machine learning algorithms. I'm excited to build on the knowledge this course has given me.

A very practical course for machine learning. By this course, one can get familiar with sklearn and pandas basic operation! The last assignment is a challenge for me. Thanks teacher for this great course!

Very good lecture for beginner:easy to understand.

Also good assignment: force you to use what you learned in the course.

The discussion forum is helpful when you meet difficulties in assignments and quiz.

Very practical introduction to using Python for machine learning - less focused on theory and more focused on how to use the sklearn library and proper use cases for different classifiers and regressors.

The course offers you a lots fot tools the face ML problems. There are few errors in the notebooks, but everyting is well documented in the forum. Good overview to represent data and train basic models.

The course is well structured and covers all the most important topics. The programming assignment could be a bit more stimulating. Overall I'd recommend this course to everyone who's interested in ML.

Fantastic course! Great foundation on scikit-learn. Really focused on APPLYING machine learning with just enough information about the models themselves to understand what's going on behind the scenes.

Pretty good for those who are not too familiar with all the statistics that happens "under the hood" in a machine learning algorithm. The name "applied" suits very well in that way. Congratulations!

An excellent resource to immerse yourself into machine learning methods. Professor Kevyn explains key concepts in the most intuitive way possible. It does require some previous experience in Python.

Complete course about (mainly) supervised machine learning. Several classifiers and regressors and explained and compared. Appropiate assignments to test the understanding. I recommend this course.

Great experience i learned a lot in machine learning in python with different terminologies used in applied machine learning. I understand each and every topic which was told by Kevin Collins Sir!

Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!

the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action