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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

2,667 classificações
500 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


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!


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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276 — 300 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por Ashwin V

Oct 11, 2019

Best course

por Rizwan M

Oct 13, 2019

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

por Weiqun T

Sep 23, 2019

This is a very good basic course for machine learning. I got the basic ideas and skills for it.

por Connor B

Sep 24, 2019

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

por Robert J C

Oct 28, 2019

It gets harder but fun...R, as well Python and Matlab, can do AI well.

por Yadder A G

Oct 31, 2019

It's the best course I've taken. It has all the basics about machine learning algorithms and more.

por Dale H

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.

por Sakib S

Mar 15, 2019

Include more swirl practice problems.

por Jiarui Q

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.

por César A

Jul 26, 2018

Very interesting course. May be a little bit harder than the previous ones but it could be done.

por Kamran H

Feb 18, 2016

Pretty good overview of how to build some types of machine learning models through the caret library in R, but not much in terms of the theoretical underpinnings or why one method is better than the other or where it is most suitable.


Oct 07, 2016

Nice Course for every New candidate

por Yuriy V

Mar 10, 2016

I liked the course and found it informative, but wish there were more stuff on unsupervised learning neural network algorithms (SOMs). Learning about most used algos are great, but would also like to know other machine learning algos that are used concurrently.

por Romain F

Mar 22, 2017

Good course on the whole, learned a lot and enjoyed it, but it would need to be updated and corrected (certain bits of code don't work as they did when the course was produced, which can be pretty confusing). Would be nice also to add some more content at the end of the course : the lecture about unsupervised prediction felt rushed, and a proper conclusion opening up to the rest of the field would be useful. Anyway thanks again for this wonderful learning opportunity, keep it up ! Cheers

por alon c

Mar 10, 2016

Great Course, will be nice to have more projects to see how it goes with different data

por Robert O

Jul 27, 2017

The course subject matter was great but like the course 6 & 7 scenarios i found the lectures didn't reiterate or reinforce key takeaways that are easily confused. For example is cross validation when you split the data into a training and testing, when you have a separate unknown results set to test final training model on. Or does it require doing folds and then breaking each of those up into training and testing chunks. Or why is it not okay to use a model training function that internally does cross validation similar like randomForest documentation suggests. Also things like what the prediction accuracy implies in contrast to the model oob [ in ] sample error estimate and if that estimate is akin to the 1 - prediction accuracy on test data set, i.e. out of sample error estimate. Seems like liitle coverage was given to whether or not there are well known training models to use or if you literally need to try and compare the 1/2 dozen or so common ones out there every time to find out which one to use for a given dataset. Also left confused about overlapping use of words classification model training, i.e. are they synonyms for the machine learning based functions we use to try and fit models to data.

por Carlos C

Aug 12, 2017

Excellent content so I give 4 starts. I stat less because the trainer speaks too fast.

por Eric L

Jun 02, 2016

Great course, very high paced with a lot of information. would have been great to add two more weeks and another project to use more machine learning

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 has given a opportunity for customization ! thank you Coursera

por Bassey O

May 03, 2016

Very informative course.

por Jikke R

Aug 11, 2016

Very enjoyable and generally quite understandable introduction to machine learnings with hands-on approach through the course project. It was a bit too fast-paced and generic for my liking, but many options were offered and highlighted for finding additional learning documents and courses to be able to deepen the knowledge acquired in this course.

por Daniel U

Feb 17, 2016

Fast paced and little focused on the algorithms but quite useful overall.

por Tongesai K

Feb 08, 2016

Very good course. I am very knew to this topic but am sure will find a lot of application in my speciality - geophysics

por Yew C C

Feb 04, 2016

Wish to have more systematic structure, detail information and hands-on exercises.

por Nilrey J D C

Dec 01, 2017

Good introduction to machine learning