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

4.6
stars
8,652 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

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!

JL

Aug 19, 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.

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1376 - 1400 of 1,574 Reviews for Applied Machine Learning in Python

By Abhishek R

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May 26, 2018

Needed a better retrospect on final/week 4 assignment

By Varun S

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Aug 29, 2022

Lot's of problems with automatic grader. Please fix.

By Alexander C

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Mar 11, 2018

Good introductory course. A lot of material covered.

By Dr. F T

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Aug 17, 2018

Good but I was expecting much details in some area.

By KOSHAL K

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Mar 1, 2020

Its a very good course for an intermediate level.

By Vinay P d L R

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Sep 26, 2017

goes too fast and too shallow to deserve 5 stars

By Wissal A

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Feb 19, 2023

Tests the aspects treated in the learning phase

By Adesh T

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Feb 8, 2022

It was amazing journey to complete this course.

By Prerna A

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Apr 27, 2021

The course is planned in a very structural way.

By Anendra G

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Apr 30, 2018

Awesome theory about machine learning concepts.

By Catherine M

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Feb 28, 2021

Nice course. A lot of ML models get presented.

By harsh a

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Feb 3, 2018

Good course.

Thanks to entire team

Harsh Arora.

By Tianyu Z

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Jun 19, 2019

Some concepts should be introduced in detail.

By Ujjwal O

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Jul 5, 2023

more visual explanations would be better!!!!

By Amita A D

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May 17, 2018

Need more information about more algorithms

By CHAPPIDI S N B

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Jan 16, 2022

Excellent way of teaching and learned well

By Souvik M

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Jan 23, 2022

where is my certificate?????????????????

By Ruben W

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Sep 8, 2019

Best course so far in this specialisation

By Alan F

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Feb 28, 2018

Good course but there's a lot of material

By Abdulwaheed M

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Jun 17, 2020

Teaching is very good and it is helpfull

By Alperen B O

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Dec 15, 2020

I get late feedback for lab assignments

By Ramya K

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Jul 15, 2019

Well-organized but assignments too easy

By Supratim D

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Aug 10, 2017

Very informative but bit too difficult.

By ROHIT J

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Aug 2, 2020

very helpfull.thanks for creating this

By Xiang C

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May 12, 2020

It's good to learn how to use sklearn.