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

4.6
stars
7,573 ratings
1,384 reviews

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

OA
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

FL
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 1,367 Reviews for Applied Machine Learning in Python

By KHADE R N

May 9, 2020

First two courses of specialization were so good, but I am disappointed by this one i.e. Machine leaning. I know this course is applied but then also advice for others, this is absolutely not for beginners, because there is too much rush in this one. I didn't understand 60% of things because new concepts are taught one after another without deep understanding and mathematical concepts that how it is working.

By ALONSO A R P D A

Jul 11, 2020

Sorry by bad writting, english is my second language, but:

Again, the videos and suggested reads are not sufficient to learn all that is needed in assingments or in real life application. Doing others courses in coursera like courses offered offered by University of Macquaire turn more clear that this course is so hard to learn because there's less things that what is actually the subject

By Gregory O

Sep 25, 2017

I was excited going into this course because the others in the series were taught well and I had learned a lot. Unfortunately, this course greatly disappointed. The lectures were dull, included a lot of mistakes, and did not cover most of what was expected during the assignments. All in all, this course was a waste of time versus just learning scikit-learn on your own.

By Shubham N

Aug 23, 2020

Not happy & satisfied with the assignments. Whenever I tried to submit, always error occurs, mostly files does not exist. Went to forums though, but files are kept elsewhere, especially for Assignment 4. Had to specially download the file and uploaded in the project directory just to work. Need to have proper file arrangements before starting the assignment.

By Nahuel V

Aug 3, 2020

I am not a big fan of this course. The assignments were too easy up to the last one that was too hard. There is no moderation in the forums, you can ask a question and nobody will answer.

By Subhadeep B

Aug 20, 2020

The instructor makes me sleepy. The autograder runs outdated versions of many packages and was last updated in 2018. Although the mentors are always active in the discussions forums.

By Thomas M S

Feb 9, 2018

I do not have the impression after this course that I have reached a level of familiarity that I will continue using the content of this course. Disappointing.

By Dror L

Nov 25, 2017

great topic, poorly presented. material not well divided among weeks. lots of repetitions. lack of hands on practice until the very last task.

By Kale H

May 31, 2020

Autograder is poor and professor is hard to listen to. You're better to just do a YouTube tutorial, like Codebasics.

By Stephen O

Aug 25, 2020

Desperately in need of an update as much of the code is no longer up to date/broken.

By Keshav B

Jan 2, 2020

Instructor tell the thing which are far beyond from asignments and quizes

By Mohamed R

Mar 27, 2020

one of the worst courses i ever had

By Frank A N

Nov 19, 2018

It was too easy

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 Rachit G

Jul 30, 2020

The instructor is very very boring

By Deepalakshmi K

Jun 19, 2019

Dude

By Kevin L

Jun 24, 2017

A great introduction to the practical side of machine learning, particularly if you have already taken Andrew Ng's course. It covers a *lot* of material and the pacing is *very* fast. Week 2 is particularly long, and if you are still a student/working it may take an extra week to complete the course. Quizzes and assignments are not terribly difficult, but be careful of the project assignment in Week 4 (though the bar for a 100% is quite low!). Finally, the accompanying Jupyter Notebooks are very helpful and there are many helpful links to outside resources as well.

A few of the lecture videos feel like an early draft rather than production-quality, with lots of time spent on repeating phrases. The instructor mentions things to be covered "later," but that "later" never comes (for example, in discussing Grid Search). For some background, this course appears to have been repeatedly delayed before its release. To me, is understandable that the creators wanted to get this course out given the demand, but the rush is felt.

Ultimately, however, this is still an excellent introduction to Python Machine Learning, and I do feel the course is well worth taking. Just be prepared to do some more individual learning; however, shouldn't one always be for an online class?)

By Clément A

Feb 3, 2021

TLDR : This is truly an EXCELLENT course if you already have a good theoretical basis in machine learning and good skills in python programming. Otherwise this will not be a pleasant experience for you.

As far as I am concerned, I worked it along with several books and this course helped me learn quick and effective hands-on machine learning skills, to complement my theoretical knowledge. If you are a complete beginner in ML, this is clearly not a stand-alone course and you will need, for example, to refer to either Andrew Ng's coursera course on machine learning or to Christopher Bishop's book (as I did). Overall, I consider this course has helped me a lot and I learned a huge amount of useful things and good practices. I now feel confident enough to apply for jobs in the ML field, which is what I enrolled (and paid) for. Nevertheless, I would have appreciated a dedicated section specifically on how to handle categorical variables. This matter is not really treated throughout the 4 weeks and I think it would have been a better choice to include it instead of the very superficial optional introduction to Deep learning. Anyway, thanks for putting up this quality course, it was a very good experience to me.

By Luis G A B

Apr 12, 2019

Muy agradecido, mis felicitaciones al Profesor Collins-Thompson, se muestra como una persona amable, dinámica y con alto grado de conocimiento, gracias a sus enseñanzas estoy aprendiendo más sobre el proceso de machine learning, siento que aun me falta mucho por recorrer, sin embargo, a lo largo de este curso aprendí los métodos, tipos de modelos, herramientas tanto para clasificación como regresión enfocándome en el área. De igual forma la literatura es muy interesante, se encuentran artículos que al leerlos vas comprendiendo como ha sido el proceso de transformación en este campo y gracias a esto, se me han ocurrido ideas que me gustaría compartir o estructurar para evidenciarlas de manera mas formal.

Muchas gracias por el apoyo, gracias por las observaciones y anotaciones dentro de los foros de discusión, siento que puedo seguir aprendiendo mas y es por eso que estoy agradecido por mis conocimientos adquiridos, los cuales siempre puedo retroalimentar viendo el curso nuevamente cada vez que lo considere pertinente.

By Stephen K

Oct 3, 2019

5 starts for content. The lecturer and slides were good. The assignments were often difficult and took many hours longer than the stated 3-4 hours. Assignment 4 was particularly heavy in time. I finished the course feeling equipped and confident enough to take on straightforward machine learning projects from start to finish. I've dropped a star because the autograder uses an older version of Python and older libraries, which meant I had to spend around 8 hours re-engineering my *correct* code to conform to old libraries.

Addendum: I've uprated the course to 5 stars after having just completed the fifth, optional week on unsupervised learning. It's unassessed but does give a nice introduction to the subject. Thanks!

By Jack O

Apr 25, 2018

Though I would have liked a bit more insight into the actual algorithms behind machine learning, this class did a great job of giving us problems and forcing us to be resourceful and hunt down the answers, whether via course forums, Stack Overflow or other random Googling. We were exposed to a ton of different algorithms and libraries, and we got to experience the whole spectrum of data science: data importing, cleaning, exploratory analysis, feature selection, model selection, parameter tweaking and even some visualization. It was a lot of fun: challenging at times, but oh so rewarding in the end!

By Anne E

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

By Bart C

Oct 9, 2018

This course is excellent. It contains a great deal of instruction each week (1-2 hours), and it also has many supplemental references for people who want to go deeper. The quizzes are actually very challenging, and require study of the material. The assignments were easier for me than the other courses in this specialization, but they were focused on application of the material to real world problem, which is the purpose of the course. The final assignment is very instructive and challenging. The instructor is very knowledgeable, and teaches in a thorough, but easy to follow, manner.

By Susmit I

Dec 28, 2020

The course was great. The final assignment was especially useful as it was almost completely unguided and gave us a dataset which is unlike the tidied up, dummy datasets you'd find in online courses. So it was, by some means, an independent project. The data was messy, full of errors, and maybe downright ugly. We needed to clean the data and do quite a bit of preprocessing to get it in a shape suitable for fitting a machine learning model. The project gave a taste of how a real-world machine learning project might be taken on. Thank you very much, Professor Collins-Thompson!