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Practical Machine Learning, Johns Hopkins University

4.5
2,327 ratings
449 reviews

About this 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

By AD

Mar 01, 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.

By DH

Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

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441 Reviews

By João Freire

Feb 14, 2019

Very good course. Clear explanations and examples give a good overview of the foundations of Machine Learning. After this course the student can build Machine Learning models.

By Raul Martinez

Feb 12, 2019

The class is good but it is too simple. I expected the professor will provide more detail about the models. This is just an introduction and weak for a specialization.

By Avizit Chandra Adhikary

Jan 31, 2019

A very good course giving brief descriptions and applications of some of the used statistical and machine learning algorithms.

By Philip Erik Wikman Jorgensen

Jan 30, 2019

Jef leek explains to fast and the theory behind the different algorithms is scarcely explained.

By Daniel J. Rodriguez

Jan 17, 2019

Seems like a lot to pack into 4 -weeks. Should really be named introductory machine learning. Needs more depth and better development of the intuitions associated to each algorithm class to match the expectations.

By Mohammad Abuarar

Jan 17, 2019

Wonderful course and instructor, it was the best in the specialization courses so far.

One note is that for most of the methods the explanation was too much precise and short and needed to reinforce it by extra material

By David Robinson

Jan 14, 2019

Great introduction to Machine Learning in R. Concepts explained very clearly and project gave opportunity to test out the concepts introduced to real data.

By Alex Fleming

Dec 30, 2018

A fine introduction, but there are much more engaging and better quality courses out there...

By Luis Manuel Murillo Reyna

Dec 24, 2018

very good

By Gaurav Bhosale

Dec 22, 2018

Loved the course