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

4.6
stars
8,716 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

DB

Oct 22, 2020

EXTREMELY USEFUL AND GOOD COURSE, CONGRATULATIONS TO ALL THE PEOPLE INVOLVE.Honestly, I never thought I could learn so much in an online course, excited for the rest of the specialization

PS

Apr 2, 2018

Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!

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By SHREYASHI D

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Sep 17, 2020

great

By Richard Z

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Sep 3, 2019

Good.

By Xia l

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May 16, 2019

GREAT

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Nov 12, 2018

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Mar 16, 2018

great

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Dec 24, 2025

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Dec 23, 2025

okay

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Dec 23, 2025

None

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Dec 21, 2025

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Dec 13, 2025

Best

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Nov 28, 2025

GOOD

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Nov 13, 2025

nice

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good

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Mar 20, 2025

good

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Dec 23, 2023

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Jan 1, 2023

good

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Mar 13, 2022

nice

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Feb 8, 2022

nice

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Jan 25, 2022

good

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Jan 13, 2022

good

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Nov 9, 2021

good

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Nov 4, 2021

good

By Byron

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Oct 30, 2021

nicd

By karra A

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Oct 28, 2021

Good

By SYED A A

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Oct 4, 2021

GOOD