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

4.6
stars
8,724 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 19, 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.

DB

Oct 23, 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

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By SYED A A

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Oct 5, 2021

GOOD

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Aug 13, 2021

good

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May 4, 2021

bala

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Nov 4, 2020

Nice

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Oct 30, 2020

Good

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Jul 25, 2020

good

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Jul 8, 2020

good

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good

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May 18, 2020

nice

By Miriam R

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Dec 27, 2019

good

By Light0617

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May 13, 2019

nice

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Jan 10, 2019

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By Jimut B P

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Oct 8, 2018

Nice

By Yi-Yang L

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Jul 3, 2017

Nice

By SURAJ K

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Jun 23, 2020

osm

By Shilpi G

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Jun 2, 2019

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By Raymundo L L

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Dec 7, 2025

OK

By DURGE A J

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Aug 28, 2024

ok

By Magdiel A

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May 11, 2019

ok

By PREDEEP K

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Nov 25, 2018

ok

By Jintao M

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Feb 2, 2023

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By Souvik G

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Aug 24, 2021

5

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Jun 17, 2021

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By Andrew G

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May 17, 2019

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