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

8,294 ratings

About the Course

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

Top reviews


Oct 13, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!


Sep 8, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

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1251 - 1275 of 1,510 Reviews for Applied Machine Learning in Python

By Ekun K

Jul 16, 2020

This is a great course. I recommend using the Introduction to Machine Learning book to complement the lecture videos.

By Wynona R N

Jun 23, 2020

Good introduction course on machine learning algorithms. The books and the readings are recommended to look through!

By Amanda V

Jun 2, 2018

You will learn a lot. But the course is a little bit fast for regular students. Assignments deal with real problems.

By Rohith S

Nov 16, 2017

A few more code examples would have helped better understand various packages provided by Python and how to use them

By lcy9086

Feb 2, 2019

Great course on doing machine learning use sklearn and put little but enough explanation of the theories behind it!

By Alexandr S

Feb 24, 2019

It would be nice to have more practical assignments like the last one! Anyway it was very interesting! Thank you!

By Bharat G

Aug 30, 2017

Amazing Course but Please add some more theory and concepts in Neural Networking.Overall it is a good experience.

By Alpan A

Nov 27, 2019

Very good curriculum with a hands on project. However thera are some limitations with the platform with grading

By Amine T

Jun 21, 2017

Complete course on supervised learning

Would be nice to cover PCA and unsupervised learning in the assignments

By Andres V

Oct 16, 2020

the final assignment was too hard compared to the other assignments and the contens given in the last module


Feb 9, 2019

A little dated. Overall a good introduction. The informal explanation of SVM was particularly effective.

By divya p

Sep 4, 2020

course is very informative with hands on details, assignments and quizzes are very useful for assessment

By Maxim P

Sep 15, 2018

Nice there could just be a bit more of a case study to see the difference and decision ways in practices

By Jesús P

Jan 5, 2018

great course but could be improved with a better explaining of the class on board for abstract concepts.

By shashank m

Jul 16, 2019

Very intuitive course...and carefully designed so that it does not overwhelm the students with details


May 11, 2018

Compared to previous two courses in this specialization, this course need more time for self-learning.

By Justin M

Apr 11, 2018

Great course overall. Only reason for 4 stars is some of the assignments could use a bit more clarity.

By Manjeet K

Sep 14, 2019

Easy to learn the course, just be focussed. Its an applied ML course, not to expect any mathematics.

By Ulka K

Feb 27, 2020

I found the dataset in the last assignment difficult to interprit. I was hoping for a simpler one.

By Vishwa M

Sep 3, 2021

Course Content was excellent. I really learned a lot. Assignment 4 was a hassle to submit though.

By Stephen R

May 8, 2018

Wish there were a little more theory, realize it's an "Applied" course but still seemed lacking

By J N

May 23, 2021

Teaching by the professor is very good and i learnt every thing from scratch thankyou coursera

By Pierre D

May 1, 2021

Interesting course. Last exercise allows understanding how to use ML, when you are all alone.

By Michel H

Jan 23, 2020

helpfull, but so many information in little time. Difficult to get clarified the ideas behind

By Samantha

Apr 5, 2020

Very great courses ! It helps to deepen my knowledge in Machine learning. Very recommend it!