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Practical Machine Learning, Universidad Johns Hopkins

4.5
2,343 calificaciones
450 revisiones

Acerca de este Curso

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....

Principales revisiones

por 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.

por AS

Aug 31, 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

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442 revisiones

por Anuj Parashar

Feb 21, 2019

This is the most interesting of all the courses in this specialization. Sometimes the content covered can be overwhelming. But the end result in the form of project assignment is worth all the efforts.

por 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.

por 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.

por 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.

por Philip Erik Wikman Jorgensen

Jan 30, 2019

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

por 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.

por 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

por 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.

por Alex Fleming

Dec 30, 2018

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

por Luis Manuel Murillo Reyna

Dec 24, 2018

very good