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

4.6
stars
6,734 ratings
1,212 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

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1051 - 1075 of 1,192 Reviews for Applied Machine Learning in Python

By Sang L

Jul 28, 2018

Speed kinda fast but maganeable. Need more detiailed notes/slides

By vatsal m

May 26, 2020

Some of the assignments have bugs in them please rectify them.

By Jose I B L

Jul 31, 2020

Good coure, need more feedback in the quizzes and asigments.

By Anuhya D

Oct 14, 2019

pre-processing and unsupervised learning needs more emphasis

By Deleted A

Jul 13, 2018

there are some gaps which is really difficult to understand!

By Xingyu W

Oct 14, 2019

Need a better configuration for homework data file loading.

By DENIS R

May 23, 2020

allowed me to hone my knowledge of machine learning models

By Jason A

Jun 26, 2018

This course was tougher than expected, but I learned a lot

By Bernardo A

Jun 08, 2017

Great content and good assignments! Learned a lot from it.

By Venkata S M B

Jun 02, 2020

Decent course. I'd call this, 'Intro to Machine Learning'

By Antti H

Oct 23, 2020

Good course, but the labs have quite a few bugs in them.

By Wang Y

Feb 16, 2018

Good, despite some confusions in the lecture and quiz.

By Tangudu S S

May 23, 2020

Got a very clear picture of ML usage in Data Science.

By Yash B

May 07, 2020

It was little bit difficult specially the assignments

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