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

8,290 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


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


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!!

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76 - 100 of 1,509 Reviews for Applied Machine Learning in Python

By Will W

Apr 24, 2021

Maybe this was once a decent machine learning course, but clearly in the last several years its administrators have abandoned it, and it is now in a state of neglect. All the assignments have bugs and errors which are never fixed. There are hundreds of forum posts with students who are confused by these errors but most of them go unanswered. When a moderator does answer a post (this happens very sporadically because the course has "limited moderation" aka no one is helping students), its only to point out previous posts with work arounds to the bugs. All questions as to why these bugs aren't fixed, saving everyone untold amounts of trouble, are ignored. I don't know if anyone will see this as I suspect most reviews on this site are fake, but please do not take this course if you value your time or money, its creators no longer care about it and are using it as a money machine they can run without any effort or interaction with students. U of M should be ashamed to have their good name on this.

By Jeff S

Jan 1, 2021

Impossible to complete the quiz and assignments without EXTENSIVE self-learning from other material. So, while the quiz and assignment forced me to find the information I needed by googling and reading and buying books, the course material itself is so high altitude as to be completely useless. I only finished because I used trial-and-error and google to pass. I learned nothing from the course, but I learned plenty from the Internet. I'm glad my company is paying for this and not me.

By Vipul S

Apr 28, 2022

This course has only given me endless amounts of self-doubts, frustration and misery. The instructor reads some script from start to end, shows some screenshots of the code and done. Then they give us unnecessarily complex assignments. Save your money and time, stay away from this course.

By Rachit G

Jul 30, 2020

The instructor is very very boring

By Deepalakshmi K

Jun 19, 2019


By Arek B

Jan 16, 2023

A bit more difficult that the first 2 courses, but it is understandable, as both the underlying math and the Python libraries are not simple this time. The Professor tries his best to introduce us to the various mathematical models and, at the same time, to show us valuable results in the Jupyter lab, all of this in the limited 6-20 minutes span of the lectures on Coursera. Given this, he does a really good job. You see that he would like to show us much, much more, but he simply cannot, due to the limitations of the platform. There are many valuable, custom methods (functions) available in Python in the lab, in form of custom libraries. For example for plotting, comparing, etc. It means that someone really spent time preparing all of this, thus it is much appreciated. Assignments ask tricky questions from the lectures, so be sure that you watched a full lecture at least once and that You were focused ;-)

By Jakob P

Sep 2, 2017

Fundamental, but still thorough, course in applied machine learning using Python. The lecturer is really good, and the quiz/problem sessions are challenging, but sufficient information is provided in the videos -- a HUGE improvement compared with the first two courses in this specialization.

By Aashish P

Dec 20, 2022

Fantastic course. Even though it is an overview style course for many of the later topics, the instructor never failed to compensate shorter theoretical sections with practically applicable advice. Plus, the optional readings are the best in the entire specialization.


Dec 30, 2022

I really like the course. It serves as an introduction to all basics on machine learning, and the practice it's really useful. Totally recommended, even better than subjects related at universities ( I am a university student and I can prove it)

By Y. N

Jan 5, 2023

Very wide scope and covers both practical and theoretical aspects (although I wish they spent a bit more time on the math).

Way better than Stanford's course that doesn't teach you anything practical and treats you like a high-school student.

By NoneLand

Jan 21, 2018

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!

By Alan H

May 8, 2019

Great course for the applications of machine learning. While I wouldn't recommend for someone with no ML experience, this was a great course for an R user trying to learn more python!

By Rami A T

Jun 6, 2017

Very helpful and well-structured course, clear lecturing, and high-level assignments. I hope, however, if it can be offered another course specialized in unsupervised learning in ML.


May 5, 2020

Great Course. I love the way it is designed, delivered. I learned a lot. The most important part is that I enjoy every bit of the session and completed everything less than a week,

By Ravi M

Feb 8, 2020

Course was designed in a well structured manner and the basic concepts were covered for Regression and Classification. Many many thanks to University of Michigan for creating it.

By Malvik P

Oct 30, 2019

The course is awesome. Professor Kevyn Collins Thompson, explains the topics with examples in python which makes content easy to understand. It is the best course for beginners.

By Vishwanath D

Aug 6, 2017

It is very good blend of study and practical assignment. Assignments were very well designed to greatly enhance the understanding about the things learned in the video lectures.

By Rob N

Oct 14, 2017

This course was challenging and extremely interesting. The long and detailed lectures and excellent lecture notes covered the material very thoroughly for an online course.

By Karthick T J

Jul 17, 2020

ML is a wonderful course.I learn new concepts with hands on experience.Each and every algorithm concept is clearly explained .I learn how to handle real time data set.

By Raga

Jun 9, 2017

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

By Eduardo L L

Nov 12, 2021

It's a really good course. I teaches you the basics of many ML algorithms. I really recommend it for begginers

By Chenmin S

Aug 18, 2022

It covered a lot of important topic/ concerpt. Very structure program.

By Jun-Hoe L

Jun 3, 2020

My actual rating is 3.5 stars. This is the best course yet in this Specialization.

Pros: I prefer Professor Collin-Thompson's delivery compared to Professor Brook in the previous modules. I think he gives a good overview and sufficient depth for an applied course, compared to Professor Brooks which I find to be quite superficial most of the time, and weirdly detailed in other parts. Assignment is good enough for reinforcement learning and definitely better planned. I also appreciate the link to additional readings which are quite informative.

Cons: Assignment auto-grader. This is still the biggest letdown of all the courses in this specialization Codes which work on your laptop or suggested elsewhere on Stackoverflow etc fails to pass the autograder, so 30-40% of the time of the assignment is spent on wrangling the code to pass the autograder.

Note: If i haven't taken a Machine Learning course by Professor Andrew Ng, this course would definitely be much harder. This course doesn't go to much into the background knowledge,and they mentioned this many times. But I appreciated the applied aspect, since this was what I was looking for.

By Oliverio J S J

Feb 4, 2018

This course is an survey on how to implement many machine learning techniques using the SciKit Learn library. Following the course, you can learn several interesting details about how to work in the field, but it is important to take into account that it is not possible to learn the algorithms during the course, since a huge amount of material is covered during a short time; to make the most of the course you have to know them in advance. It bothered me to discover that the course was planned for five weeks but Coursera has reduced it to four, removing the possibility of practicing exercises on unsupervised learning.

By Andrew B

Mar 24, 2021

Overall a good course; I learned a lot. But hard going at times for someone new to Python and Jupiter Notebooks. The time estimates for the module assessments are way under (maybe reasonable if you are already a Python expert and have some familiarity with the relevant libraries, but that's not my situation). File location mismatch between Assignment notebooks environment and submission / assessment environment was very frustrating.