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

4.6
stars
8,514 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!!

AS

Nov 26, 2020

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

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676 - 700 of 1,550 Reviews for Applied Machine Learning in Python

By Marek S

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Sep 29, 2017

Useful, practical use of sklearn to machine learning tasks

By Matias B

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Aug 14, 2017

Challenging and rewarding. Wouldn't have it any other way.

By Dipanjan S

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Jun 23, 2017

Excellent clarity, recommended for getting started with ML

By MOHSIN K 2

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Oct 29, 2021

Learned a lot!! Thanks coursera for this wonderful course

By sung w c

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Sep 25, 2017

Very well organized and useful for hands-on application.

By Hiroki U

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Nov 29, 2020

Last assignment was very good for understanding ML task.

By Yujin Y

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Oct 13, 2022

I could learn a lot from this course. a great course.

By Joga j

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Jan 24, 2022

very good course and content.so many practice labs good

By Mehrar I

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Oct 10, 2020

this course is really helpful to learn machine learning

By SAFVAN M S P

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Aug 17, 2020

Amazing course, full of insights. Very well structured.

By Thales A K N

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Jul 3, 2020

Best Course in the Specialization!!! I learned so much!

By Maryanne K

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May 4, 2020

Great! Fun and useful course. Concepts explained well.

By Ankush G

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Jan 14, 2020

A good stepping stone towards a career in data science.

By Fei W

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Nov 6, 2019

The course is very well structured, highly recommended!

By Nikhil N

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Jul 6, 2021

Wonderful Course but slightly difficult for beginners

By Kunal K

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Apr 22, 2020

it good to basics and devloped the skill in that field

By Ajay S

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Jan 29, 2019

great course thanks for financial aid for the course .

By Kristin A

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Jan 12, 2019

Great intro to the tools of machine learning in Python

By Walter M

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Jul 29, 2018

Good class. The asignements made me a better engineer.

By Alonso S A

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Nov 10, 2017

Very usefull, easy to understand and full of examples.

By Кочеткова А М

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Dec 8, 2020

Interesting lectures, everything is clear, thank you!

By Edgar G

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Sep 17, 2020

Good Content. Interesting and challenging assignments

By Puchakayala S J

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Jun 9, 2020

This is the best course of the best one's. Thank you!

By Varun R

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Jun 1, 2020

Thank you coursera for financial aid and such content

By Dongxiao H

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Jan 31, 2018

It is helpful for me to be familiar with scikit-learn