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

4.6
stars
8,670 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

MB

Jun 18, 2017

Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.

S

Oct 23, 2021

It was good learning Machine Learning thru Python as using Python libraries like Pandas , Tensorflow,.etc made the work easier. Hope to do my masters in Machine Learning . Happy Learning <3

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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 13, 2018

great

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Mar 17, 2018

great

By Mayank Y 2

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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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Mar 20, 2025

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Dec 23, 2023

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Jan 1, 2023

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Mar 13, 2022

nice

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Feb 8, 2022

nice

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Jan 26, 2022

good

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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

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Oct 4, 2021

GOOD

By ANDRES C F G

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Aug 12, 2021

good