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Voltar para Aprendizagem Automática na Prática

Comentários e feedback de alunos de Aprendizagem Automática na Prática da instituição Universidade Johns Hopkins

4.5
2,679 classificações
501 avaliações

Sobre o 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....

Melhores avaliações

JC

Jan 17, 2017

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!

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.

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301 — 325 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por Igor H

Sep 10, 2016

Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.

por Hernan S

Dec 13, 2016

The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.

por Shobhit K T

Dec 18, 2017

Great course with practical insights on machine learning

por Roberto G

May 21, 2017

Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice

por Manuel C

Dec 26, 2017

I feel I could have master the subjects better

por S M H R

Feb 10, 2016

A good course where you can learn how ML algorithms work practically.

por Raymond M

May 02, 2018

pretty good!

por Kevin S

Mar 03, 2016

Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.

por Swapnil A

Jun 09, 2017

The course covers few important topics in R like cross validation, decision trees, random forest etc. which comes in very handy for a data science aspirant. It expects the participant to have a descent knowledge in R. Overall, I am pretty satisfied with this course. Thanks!

por Greig R

Nov 14, 2017

Good course, I learnt a lot. It does need to be updated with more modern versions of software.

por Simon

Oct 25, 2017

This course is brief but it has the 2 best ingredients for having a really decent first step in Machine Learning:

1) It covers a broad group of different algorithms

2) It provides reference material for those in which you want to get deeper.

Really good job in this course.

por Lilia K R E

Mar 30, 2016

Muy buen curso :)

por Carlos R

May 27, 2018

Great Course! I have learned a lot of new things.

por Carlos S

Jan 31, 2016

First and foremost I'm so thankful for the exposure to so much material in such a condensed schedule. Very good class. Even though I had to muscle my way through it.

I think the class could be improved with one additional discussion thread for the project.

A guide similar to the ones created for Inferential Statistics and Regression would also have been very helpful.

I benefited immensely from reading parts of the book "An Introduction to Statistical Learning" while taking this course.

por Christian W

Jan 31, 2017

First 3 weeks are manageable and the final project is great! I had a lot of fun :)

por Anant S

Jun 30, 2017

good course for initial understanding of machine learning. SVM can also be included.

por João R

Aug 20, 2017

Got confused how to perform cross validation and when. Other than that, very practical. Great job.

por Lucas

Jun 03, 2016

This course allows you to implement practical solutions using machine learning algorithms without having to know the mechanisms behind the calculations in detail. Unfortunately questions in the discussion forum were quite rare and many questions were not resolved during this course.

por Orest

Jan 22, 2018

It needs more mathematical detail. Otherwise is a fairly comprehensive class, and a great tutorial on the caret package. I recommend it, if you need to refresh concepts and get some practical exposure to caret.

por Emily M

Mar 12, 2018

This course gives an overview of a broad subject. My personal feeling is that there could have been some more indepth examples/case studies to demonstrate how to apply these methods and analyse /interpret the outcomes.

por Matthew L

Jan 06, 2016

Really good overview of machine learning techniques and model evaluation.

por Lee G

Sep 22, 2017

A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.

por Utkarsh Y

Nov 17, 2016

Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.

por Rahul K

Mar 07, 2016

Really Well Structured Course!!

por Aashaya M

May 29, 2016

In my opinion this course is highly technical and demanding in nature compared with the others. The learning experience is good and coursera.org has given a opportunity for customization ! thank you Coursera