Chevron Left
Back to Applied Machine Learning in Python

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

4.6
stars
7,371 ratings
1,344 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

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!!

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

Filter by:

1276 - 1300 of 1,323 Reviews for Applied Machine Learning in Python

By BIRENDRA H S

Jun 13, 2020

there should be some low level usage of sentences for a intermediate programmers,most of times it bounces up the mind ,not able to get the required concept

By Baizhu

Jul 5, 2017

Know some existing machine learning functions and packages from sklearn, but really don't know how to improve prediction accuracy within each function.

By Matteo B

Aug 10, 2019

Assignments are not really supported by the material provided (videos). The level is not balanced. Some bugs in the assignment code as well

By Berkay A

Jul 15, 2020

This course seems hard and actually I did not like the syllabus so much. Assignments were so hard and there were some issues in Notebooks.

By Halil K

Sep 26, 2019

Good content, bad teachng staff. Though the discussion forum contributors were very helpful and should be commended for their efforts.

By Ankur P

Mar 30, 2019

Unsupervised learning was missing. The codes written in the lectures were not explained clearly. Some topics looked unimportant.

By James F

Feb 13, 2018

Good overview of methods. A bit too intense at times though, may have been better to really focus on a couple of key concepts.

By Om R

Apr 26, 2020

The course is great, but need certain improvement for assignments and quizzes. The facts should be checked multiple times.

By Darshan S

Dec 31, 2019

Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

By Mauricio A E G M

Nov 17, 2019

This course is not useful to learn from scratch, but has some good things, for example the final assignment.

By Nikola G

Jan 14, 2019

Really didn't like the quiz parts of the course. If it was up to me I would do thorough revision of these.

By Chirag S

May 24, 2020

The content was less informative and audio quality was poor. However, assignments are fun completing.

By Rohit S

May 21, 2020

The online grader needs to be updated as there is constant error showing up though our code is right

By Gilad A

Jun 27, 2017

The last assignment was super. apart for it, the assignments and the course were too easy

By Sai P

Jun 3, 2020

There were a few corrections made during the videos which ended being quite confusing.

By Philip L

Oct 31, 2017

The assignments are extremely difficult, professor is a bit dry during lectures.

By Sundeep S S

Apr 4, 2021

Only classification based ML is covered. Regression based ML is non-existant.

By Pakin P

Jan 10, 2020

How can i pass without reading discuss about problem with notebook

By Hao W

Aug 27, 2017

The homework is too easy to improve our understanding of ML

By M S V V

Jun 29, 2020

Too much of information compressed within a short span.

By José D A M

Jun 21, 2020

Too fast, yet too difficult. Needs deeper explanation.

By Navoneel C

Nov 21, 2017

Nice and Informative but not practically effective

By Priyanka v

May 8, 2020

if it is more detailedthen it will be more useful

By Sameed K

Mar 15, 2018

have to figure out a lot of things on you own.

By Andy S

Jun 4, 2019

It could have been better with more examples.