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

4.6
stars
8,758 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

JL

Aug 20, 2018

Concise and clear presentation of the material with the majority of time focused around using TDD to learn and practice concepts through developing solutions to open ended coding challenges.

AG

Aug 27, 2017

A lot of techniques packed into a relatively short course. Weeks 2 & 4 are noticably tougher than the other two, so allow plenty of extra time for assignment and quiz in those 2 weeks.

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676 - 700 of 1,596 Reviews for Applied Machine Learning in Python

By Maciej W

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Jul 9, 2018

Very informative, broad, hands-on course. Strong recommend.

By nitin p

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Mar 1, 2018

Very Interesting and fascinating Course of Machine Learning

By Biju S

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Oct 12, 2017

Very tough to finish. Big gap with material and assignments

By Amoghavarsha B

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Mar 20, 2020

Perfect course with lots of assignments and good material!

By Leonadis

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Jul 17, 2018

Good applied material to study along theoretical material!

By Marek S

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Sep 30, 2017

Useful, practical use of sklearn to machine learning tasks

By Matias B

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Aug 15, 2017

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

By Dipanjan S

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Jun 24, 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 30, 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 11, 2020

this course is really helpful to learn machine learning

By SAFVAN M S P

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Aug 18, 2020

Amazing course, full of insights. Very well structured.

By Thales A K N

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Jul 4, 2020

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

By Maryanne K

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May 5, 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 7, 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 13, 2019

Great intro to the tools of machine learning in Python

By Walter M

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Jul 30, 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.