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Learner Reviews & Feedback for Practical Machine Learning by Johns Hopkins University

4.5
stars
3,130 ratings
595 reviews

About the Course

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Top reviews

MR
Aug 13, 2020

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

AD
Feb 28, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

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151 - 175 of 585 Reviews for Practical Machine Learning

By Tristan F

Dec 25, 2019

Lectures were very clear and helpful! Professor Leek was great at breaking down the topics.

By Oleksandr K

Jul 11, 2018

Great course! However, it would be good to learn about artificial neural networks as well.

By Jean N

Aug 24, 2017

Very nice Course. I am applying it right away for Predictions in the Telecoms environment.

By Tomer E

Aug 6, 2020

Great course!

Covers basics of machine learning algorithms and how to implement them in R.

By Rizwan M

Oct 13, 2019

great course. could have explained more techniques in caret package with coding examples

By Connor B

Sep 24, 2019

Really good exposure to machine learning and builds on the previous course in regression

By Alfonso R R

Nov 13, 2018

Hands on course. Loved it. It goes a little bit fast, however, the content is ambitious.

By Brian G

Aug 17, 2017

Great course. Mechanics of the final assignment are more difficult than the work itself.

By Paresh P

Dec 8, 2020

Explained practical machine learning well, concepts like model stacking really helped!

By Sean D

Jun 10, 2020

Really liked Dr. Leek's talks, and the subject matter was interesting and kind of fun.

By Konstantin

Mar 2, 2020

Excellent course. Lots of exorbitantly useful knowledge. I`ve been lucky to start it.

By Donson Y

Sep 4, 2017

This is a fantasy course to know that how to build your first machine learning model.

By Jorge M A A

Apr 13, 2016

I enjoyed a lot this module, I'll use at my daily work some of the features I learned

By Premkumar S

Mar 16, 2019

Great course and farily challenging exercises! Thank You for putting this together!!

By Sai S S

Jul 17, 2017

Great course. Ways to curb plagiarism & cheating needs to be revisited by your team.

By Thet P S A

Aug 21, 2020

It supports a lot in my thesis. Thank you, lecturers, at John Hopkins University.

By Mary

Aug 19, 2019

Very informational with good variety of code to take back and apply to projects.

By Nikhilesh J

Mar 2, 2018

Provides a quick and dirty look at Machine Learning. An easy way to get started.

By Jeffrey M H

Jun 10, 2019

So far, one of the most fulfilling courses in the Data Science specialization!

By Ajendra S

Nov 7, 2017

This a good course, giving you the inside of the data science problem solving.

By Lei M

Aug 23, 2017

This course is demanding, but I feel my own progress which is very fulfilling.

By Johan V M

Aug 21, 2020

I loved this course. I will absolutely take more courses on Machine Learning.

By Forest W

Jan 9, 2018

Much Better than the previous courses ( Regression and Statistical Inference)

By Chris H

May 23, 2016

Great course. I really enjoyed working on the prediction project at the end.

By Marcus S

Feb 11, 2016

Great introduction to the subject with good classification examples using R.