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
6,544 ratings
1,168 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 14, 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 09, 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:

1026 - 1050 of 1,149 Reviews for Applied Machine Learning in Python

By Abhishek R

May 27, 2018

Needed a better retrospect on final/week 4 assignment

By Alexander C

Mar 11, 2018

Good introductory course. A lot of material covered.

By Tarrade F

Aug 17, 2018

Good but I was expecting much details in some area.

By KOSHAL K

Mar 01, 2020

Its a very good course for an intermediate level.

By Vinay P d L R

Sep 26, 2017

goes too fast and too shallow to deserve 5 stars

By Anendra G

Apr 30, 2018

Awesome theory about machine learning concepts.

By Harsh A

Feb 04, 2018

Good course.

Thanks to entire team

Harsh Arora.

By XJTLU

Jun 19, 2019

Some concepts should be introduced in detail.

By Amita D

May 18, 2018

Need more information about more algorithms

By Ruben W

Sep 08, 2019

Best course so far in this specialisation

By Alan F

Feb 28, 2018

Good course but there's a lot of material

By Abdulwaheed M

Jun 17, 2020

Teaching is very good and it is helpfull

By Ramya K

Jul 15, 2019

Well-organized but assignments too easy

By Supratim D

Aug 10, 2017

Very informative but bit too difficult.

By ROHIT J

Aug 02, 2020

very helpfull.thanks for creating this

By Xiang C

May 12, 2020

It's good to learn how to use sklearn.

By Jagadish C A

Sep 19, 2019

Gives good overview of ML using Pyton

By Shreekant G

Jul 17, 2019

Really taught best ML algorithms

By xingkong

Aug 09, 2017

quiz is harder than assignment.

By shreyash t

Jul 28, 2020

overalll good way to start ml

By Vaibhav S

May 27, 2020

way better than last teacher.

By Nicolas B

Jul 05, 2017

Muy buen curso, muy completo.

By 李祥泰

Aug 15, 2017

Nice courses with nice quiz!

By 刘倬瑞

Jul 29, 2017

Useful, though a little easy

By Landon M L

Jul 09, 2017

the discussion forum is good