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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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376 — 400 de {totalReviews} Avaliações para o Aprendizagem Automática na Prática

por Mehul P

Oct 03, 2017

Good ML overview.

por Stephan H

Aug 12, 2017

Very challenging course. I learned a lot. Tanks.

por Brynjólfur G J

Sep 25, 2017

Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.

por Subrata S

Mar 09, 2017

Very good course. The content can be enriched with some more technical details behind the various techniques. There needs to be 1 more course on Practical Machine Learning in the specialization as 1 course is far too less for such a vast topic.

por Vincent G

Oct 22, 2017

appropriately challenging material.

por Karthik R

Aug 07, 2017

Bit tough, but I will have to say, good introductory course.

por Piyush P

Jul 13, 2017

good context

por Pieter v d V

Jun 28, 2018

Very quick overview. If you really want to know something about it read the reference books.

por Sabawoon S

Sep 14, 2017

Excellent course, very practical. Found the project challenging as preprocessing data required some knowledge of the limitation of the RandomForest method i.e. both train and test needs to have same classes of data with similar levels.

por Saurabh K

Mar 09, 2017

Very useful course to develop level knowledge in machine learning.

por Jorge E M O

Sep 07, 2018

The course rushes over a lot of concepts and it already shows its age - however, it's a pretty solid introduction to machine learning from a practical perspective. It will provide you with a lot of ideas for further investigation and exploration and in the end you'll end up with a wide vision of the machine learning process.

por Qian W

Sep 09, 2018

need eva on my project

por Jakub W

Sep 24, 2018

Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice

por Chonlatit P

Oct 20, 2018

GREAT course! There are all base of machine learning field. The limitation is blur between basic and detail especially maths. This course, sometimes , show the maths that make you confuse if you're not familiar with them.

por Johnny C

Oct 23, 2018

It was in general nice course. However, quizzes need improvement.

por Samuel Q

Oct 24, 2018

Good course to get only the basics of machine learning. The assignments and quizzes are great but the lecture material is very brief and short. The references provided throughout the lectures are probably the best source of more information.

por Sheila B

Aug 09, 2018

I've been working my way through the whole track, and this was by far the most complex material--but it was easy to understand because the videos were so clear.

I do have one bone to pick, though: the quiz material relies on very old packages. Again and again I had to finegle something so I could answer a quiz question. That makes you guys look like you are lazily sitting back collecting money but not really doing your job as far as teaching goes. It's time for an update. How hard is it to run your quizzes on updated packages and offer answers that are current?

Aside from that, I find that you explain material very clearly and you are my first choice for picking up a new data science skill.

por Grigory S

Aug 28, 2018

A bit short on practical aspects of different models

por Terry L J

Nov 09, 2018

Lot of good material, however, on all of these courses, it would be very helpful if they were better organized for learning.

Overview of learning objectives in a step sequence for a more organized approach for learning (maybe even a process roadmap map sequencing activity to follow that you can reference back to.

Detailed information for each step in the learning process that can be followed that maps back to the roadmap.

A summary of the learning objective in the roadmap sequence.

Basically, just like writing a paper, > overview/objectives > Main topics >subtopics, etc. > summary

por Diego T B

Nov 07, 2018

Very useful. The models were very easy to understand

por Sulan L

Nov 19, 2018

I hope we can have more détails in this cours and to see how to use the algorithms for the big data. Thank you.

por Erik K

Jul 08, 2019

Very good. Learned a lot

por Sanket P

May 27, 2019

ok

por Oliver S

Jul 26, 2019

A reference solution for the quiz questions as there are in some other courses in this specialization would have been nice, since I got sometimes very different results using the newest versions of the libraries and I'd really like to know, if I made any big mistakes and it's not only because of my setup.

por Caio H

Aug 23, 2019

I learned a lot in this course, but I would recommend taking the courses in order.