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Apprentissage mechanique pratique, Université Johns-Hopkins

4.5
2,454 notes
462 avis

À propos de ce cours

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....

Meilleurs avis

par 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.

par 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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454 avis

par Andrew Chastain

May 14, 2019

The lectures and quizzes are based on old versions of R and R packages. This course needs a serious update, as some packages work differently, test answers have changed (but not been updated) and coding along with the videos results in different results. Going to the forum you can see that this has been an issue for a few years now.

par Matthew Stetz

May 08, 2019

Good introduction to machine learning. Provides pretty comprehensive coverage of major algorithms and approaches.

par Dora McAlpin

Mar 30, 2019

Really enjoyed this class and learned a lot!

par Jiarui Qi

Mar 27, 2019

It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.

par Premkumar Siddharth

Mar 16, 2019

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

par Sakib Shahriar

Mar 15, 2019

Include more swirl practice problems.

par Paul Ringsted

Mar 13, 2019

A key course everything has been building towards, some important concepts and modeling techniques are introduced. However Jeff rushes through a lot of material, and I think this would be better served as two courses with more case studies and exercises, especially as the capstone doesn't use much of this. But nevertheless a useful introduction to this topic, concepts of training vs. testing etc, different models to be used, along with the caret package in R.

par Yap Yanliang Amos

Mar 11, 2019

Instructor was clear in his explanation. Would prefer to have more hands on exercise for practice

par Bruno Rafael de Carvalho Santos

Mar 07, 2019

a quick introduction to the basic algorithms for machine learning in R

par Mahmoud Elshiekh

Feb 25, 2019

Very informative