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

4.6
stars
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

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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1226 - 1250 of 1,509 Reviews for Applied Machine Learning in Python

By Weiqi Y

Oct 24, 2017

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

By Miguel A N P

Mar 13, 2022

Vary good course, vey well explained, the only problem I used to have was about the assignments, there were some ptechnical problems with the files

By Sidharth R

May 9, 2021

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.

By Helen L

Jun 15, 2020

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

By Utkarsh S

Jun 22, 2020

Very informative course, the only issue I had was with the file locations in the assignments. Takes up a lot of time switching back and forth.

By Mariano T

May 18, 2020

There are some problems with the assignments but the course is very good. You must improve the material for the assiggnment. I love the forum

By Alireza M

Jun 4, 2022

Knowledgeable teacher but still need to improve some presentations to limit the need to get extra resources for understanding the materials

By Srinivas R

Sep 22, 2017

Good overview of machine learning topics with practical exercises in the use of multiple techniques primarily through use of scikit-learn.

By David W

Jul 3, 2017

Hands on and practical. Dr. CT and his staff have done a great job introducing Machine Learning. Where were you 20 years ago? Thank you!

By Rakshit T

Jul 10, 2018

A good course for beginners in Machine Learning. You get to the learn the basics of many techniques and their implementation in python.

By yannick t

Apr 12, 2018

Excellent lectures. However, I would have needed more guidance for the last assignment. I learned a lot, but through pain and struggle.

By M V B

Oct 9, 2020

It was a great experience learning through Coursera ,who provides best faculty for making students understand easily.

thank you Cousera

By GC

Mar 31, 2021

This course is useful, but the code is not updated, and the assignment and Module codes returned a lot of code deprecation warnings.

By Prathmesh D

Jul 15, 2020

It was a great learning with you all got little problems but solved as per instructions and they helped me through that,thanking you

By PRATIKKUMAR A P

Aug 23, 2020

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ience of machine learning using python. Very well explained algorithms and application through modules and assignments.

By Muhammad I

Aug 27, 2021

Best Course if you are searching for the applied side of Machine learning and Assignment are very helpfull to make mucssle memory

By MARCO S M H

Feb 7, 2021

excellent course, except for the last week. I think that the last part about decision trees, NN and randomforest could be better

By Dr. P R K

Jan 23, 2018

Unlike the name suggests, this course only covers the Supervised learning side of the ML. However, the supervised side is good.

By Michael S

Jun 29, 2019

Everybody has different skill levels, but this was really hard and really, really, really fast.

Did I say it was really fast?

By New_diver N

May 22, 2019

Course content is very nice and covered aptly. I feel that some where more depth was necessary to understand the algorithms.

By bob n

Aug 31, 2020

Tough, but fair weekly assessments. Lecturer is a bit on the dry, boring side. Be careful not to let you attention drift.

By BHAGYASHREE B

May 9, 2020

Other than the subtle mistakes, the overall course was very informative. I wish there were more practise exercises though

By Mohamed S

Mar 26, 2020

A comprehensive course by a wold class university,some teaching could have been better by using more interactive methods.

By DIPTI M 2

Dec 12, 2022

Overall it was a great experience.

I think the instructor should've adapted a better technique to teach more proficiently

By Amaira Z

Jan 12, 2021

Well explained course with good material in python, may be an additionnal week is needed for the unsupervised learning