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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
2,675 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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176 — 200 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por Abdullah A

Sep 03, 2017

This is my favorite course so far.

por Sumeet M

Jan 08, 2018

Nice Course

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 Mohamed A E M

Feb 08, 2018

Grat course =) im really happy about it

por Sabitabrata M

Jun 10, 2018

Good course. Good overview on Machine Learning. But to understand the concepts I had to consult external resources.

por Amit K R

Nov 21, 2017

ok

por Akash P

May 11, 2018

Great content,clarity and informative.Assignment were perfectly framed to revise all the studied concepts.

por leihong w

Dec 13, 2017

Very practical course on Machine Learning

por Greg A

Feb 22, 2018

A great course that really helps demystify what machine learning is and how anyone can use it to build prediction models and start to answer tough questions using data.

por SagarSrinivas

Sep 19, 2017

Quite Happy About it!

por Tasif A

Jun 16, 2017

Great Course. Must do it.

por HIN-WENG W

Feb 07, 2017

PML is a deep subject and this course is an excellent foundation for further studies. Prof Leek has taught brilliantly on the basic concepts of PML given the short time of 4 weeks. You need college level statistics to fully appreciate the theories of the PML's lectures.

por Philippine R

May 22, 2017

I learned so much in such a short period of time. Challenging, very hands on, great theoretical foundations!

por Mertz

Mar 20, 2018

Real practical machine learning!

por Vinicio D S

May 22, 2018

You will learn how to use the caret package and learn how to implement ML algorithms. If you want the theory behind it, you need to go to other courses

por Selim J R

Dec 15, 2016

Excellent course. I feel like i know so much already even though we scratched the tip of the iceberg. Will definitely enroll in more advanced courses.

por Rishabh J

Aug 22, 2017

All the major machine learning algorithms and techniques are provided in a way that you can begin using them right away. The course project also provides an opportunity to apply the different techniques learnt in class to a rather messy dataset.

por Yong-Meng G

Jun 20, 2017

Insightful and practical ! One of the best so far.

por Fernando L B d M

Oct 22, 2017

Awesome!

por yefu w

May 30, 2017

Great Course!

por Albert C G

Sep 04, 2016

Fun course, also practical and useful

por Sarah S

May 31, 2017

I enjoyed detailed information and was very straight forward to understand.