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

4.6
stars
8,296 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

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

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701 - 725 of 1,510 Reviews for Applied Machine Learning in Python

By LEE D D

Nov 9, 2017

Perfect and hard course than Andrew Ng's ML course!

By A A

Aug 4, 2017

Best introduction to sklearn library I came across!

By zhang y

Oct 3, 2021

comprehensive machine learning course for beginner

By Pratama A A

Jul 14, 2020

If you're beginner i suggest dont take this course

By Ameya B

Jul 3, 2020

Overall good intro to actually using scikit-learn.

By likejian

May 14, 2020

It’s very nice course to learn ML for the new guys

By Abdelrahman M s A

Feb 26, 2018

One of the best practical ML courses in the field!

By ARUN S

Nov 9, 2017

Great professor with lot of real world experience.

By ChanLung

Jul 31, 2017

Excellent Machine Learning Course for application!

By Rui J (

Nov 16, 2021

it is so much fun to write programms on your own!

By Oxana M

Dec 7, 2020

I like this cource. It gives a very good overview

By Bauyrzhan A

Nov 15, 2020

It is decent course with fair level of complexity

By Anuj P

Jun 20, 2020

tremendous knowledge for applied machine learning

By SUBBA R D

Jun 11, 2020

Very useful course especially for the beginners .

By Raúl V - S

Apr 17, 2020

Very well organized and challenging real datasets

By Prachi A

Mar 1, 2020

Amazing course for a beginner in Machine Learning

By Juan S

Feb 2, 2020

Good overview to a lot of different ML techniques

By Dongquan S

Oct 9, 2019

Very well designed course. Learned a lot. Thanks!

By Kee K Y

Aug 8, 2021

Practical and excellent course for ML Specialist

By Arturo R

Jul 15, 2021

Very well balanced between dificuty and learning

By Manan S

May 25, 2020

Awesome Teaching and Assignemts are very usefull

By Debayan M

Jun 4, 2019

A must -learn for every aspiring data scientist.

By Mischa L

Jan 6, 2018

Great course with excellent homework assignments

By Shivani R

Jul 19, 2020

Very good course. Detailed videos & explanation

By SRIHARI

Jul 18, 2017

This is good course gives in depth information.