Learner Reviews & Feedback for Applied Machine Learning in Python by University of Michigan

Much better than the second course, the materials are carefully prepared and organized, teaching staff are very helpful in solving issues, however, assignments are not so challenging, still needs improvement.

Comprehensive and interesting course in Machine Learning. The use of Scikit Learn helps to give a concrete understanding of ML as well as how many specific algorithms can be utilized in real world problems.

It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.

assignments were so good. I think there was not enough information given for the quiz tests. And also the code given was not properly explained. But the materials were so good for practice

A good introduction to algorithms available in python. I didn't give it a five stars because I 'm still confused on which algorithms to pick/use when I want to work on real data problem.

Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.

Globally, very good course - strongly recommanded

The final assignment passing was a little too east,

there not being need to use fully what I learnt.

Still,the overall course was very good, and I am willing to keep on take other courses.

This course was very good, with a lot of information and important tips for me. The instructor is good but he is long winded, so this course was very long with videos during 20 minutes.

- more technical materials, comparisons and better classified details should've been provided, especially to be more proportional to the assignments.

-again, subtitles were full of typos

The course is well balanced but the progression becomes quite agressive at Week3 and culminate at Week4 with a real life case assignment without much guidance. Great experience dough.

Amazingly explained. An intermediate Machine Learner would definitely get clarity of concepts already learned and also new concepts explained so skillfully with graphs and diagrams.

Good overview of methods in ML. Would have been nice if the lectures contained a little more mathematical rigor and explanation of why and how the various algorithms are effective.

Good course and prof.

The exam and exercise in very interesting according to what I learn in following all videos, with this i improved my level in python progamming, I recommended.

Automatic assignment grader has room for improvement. Some python code that works perfectly well when run locally or on the course web page would crash when run by autograder.

The class material is well prepared and make machine learning very easy to learn. The first three homework assignment is a bit hand-holding but the last one is really good.

Good Course, i would have liked a little bit more theory about the algorithms, but this is an applied course of ML. Projects are good and the readings are interessting!

Videos are good and had challenging assignments. I enjoyed learning new concepts. I wish we had one more week to practice more on advanced Machine learning concepts.

Very interesting, a lot of focus of statistica theory and little less (as compared to previous courses of specialization) on practical examples and implementation.

Good overall but some concepts and python functions need more explanations. Maybe 5 or 6 weeks are more appropriate for this course. It is too dense under 4 week.

better than the first two courses of this specialization for the content is coherent and the assignment is relevant to the knowledge taught in the course video.

Request: Can we have the instructions with a "translation" to "regular" English - for those of us who still have to get used to machine learning jargon? Thanks.

Code and examples were very useful. Teaching a bit lengthy and detailed at times. Overall a very good course for getting hands-on machine learning in python.

It's alright as a course focusing on applied techniques. If you are expecting more theories and understanding of the algorithms, this one may not for you

the authors can include more coding questions so as to not only help a student to learn Machine learning but also become fluent in implementing it.

Submission isnt easy often gave errors that are not due to students' faults. Time-consuming unnecessarily. The content and assignments are great.