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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
2,578 classificações
483 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

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.

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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201 — 225 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por aditya n p

May 12, 2016

Awesome Course !!

por KOALA V

Sep 25, 2017

Very interesting course

por Anitha C

Jul 27, 2017

I enjoyed working on this course. There is a lot good and

por Ravi K

Sep 29, 2016

Excellent course for beginner.

por Vitalii S

Aug 01, 2017

No swirl exercises, but last project totally worth my time.

por Carlos A C Z

Feb 12, 2018

Nice course.

por xuanru s

Jan 24, 2017

very clear explanation

por Sinan G

May 29, 2017

Very fine course in machine learning where the focus is more on the use of ML rather on the theory behind it i.e. the course title fits its contents.

por Julio G C

Feb 10, 2018

It is exactly what you need to begin in datascience.

Very good client's service if you've some problem.

por Reinhard S

May 19, 2017

ok

por Khairul I K

May 27, 2017

Good

por Tinguaro B

Oct 15, 2017

Good course about Machine Learning in R.

por Samir A G

Jan 08, 2017

Excellent course, very practical !

I am very curious about the maths so I will add some specialized certifications

por Luis A A C

Mar 27, 2018

Excellent course.

por Caner A I

Apr 12, 2017

Jeff Leek is a great professor .The delivery of the course material is very clear and covers a lot of predictive methods by using mainly R's caret package. Recommended for sure.

por Draidi F

Oct 22, 2017

intense one :-)

por Edgar I

Apr 09, 2017

Excelente curso!

por Bopeng Z

Jul 31, 2017

Good practice. Very practical skills learned

por Yatin M

Oct 12, 2017

In general, great course. But because of the strong interest in ML, I am going to attempt a detailed review.

PROS:

This course truly de-mystifies "Machine Learning". After completing the course, you will be able to programmatically use 100s of ML algorithms that have been created by others over the years. You will be able to use the Caret package in R to simplify your application, simplify pre-processing, perform automatic cross-validation/model tuning and generate various statistics about the model used by your ML algorithm. You will be able to easily estimate out-of-sample accuracy to determine if your model has any hope of working well, picking one classifier over the other, or using several classifiers to estimate outcome. You will learn how some of the heavily used algorithms in the industry work behind the scenes, and where to go to learn more about these. Several learning databases are introduced. If you tinker with them, you will be amazed at how easy R and Caret make it to apply ML algorithms. You will understand how chatbots, recommender systems, spam filters, "prediction" systems and the like work.

WHAT THIS COURSE DOES NOT COVER:

It does not cover how to write your own ML algorithms. That requires working knowledge of optimization algorithms, advanced math and probably lots of other resources.

WHO SHOULD TAKE THIS COURSE?

Only those prepared to work hard, dig in, and persevere through a lot of (sometimes difficult) material will benefit from this course. If you're not confident about your statistics concepts, not comfortable with R and databases, not comfortable with googling for parameters and techniques not directly discussed in class slides, then you will have trouble. Passing the quizes require you to refer to material from prior weeks, read online documents and look for similar solutions at stackexchange etc.

TIP FOR MENTORS:

For every week of the course, create a pinned post which says "Tips/Errata for Quiz #n". You've collected sufficient feedback from students now and know what the common issues are. Don't make them search through 100s of discussions to figure out solutions to well-known/common problems.

por Nigel M

Oct 03, 2017

very good and informational

por Raja J

Dec 26, 2017

Excellent course

por Fernando L B d M

Oct 22, 2017

Awesome!

por Sai S S

Jul 17, 2017

Great course. Ways to curb plagiarism & cheating needs to be revisited by your team.

por Lopamudra S

Feb 04, 2018

The practical machine learning course is a booster for the data science aspirant.The concept taught by the Prof Jeff Leek is easily understandable. Thank you so much Sir.

por Norberto O

Jul 09, 2018

I give 5 stars because at the end I was able to understand the different topics and obtained a deeper sense of what can be done with the tools provided, however I believe there should be one section to have a complete exercise in order to better understand how to organize a project and put together all the tools provided. Perhaps a general diagram for analysis may be good to have as well.