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Avis et commentaires pour l'étudiant pour Apprentissage mechanique pratique par Université Johns-Hopkins

4.5
2,701 notes
505 avis

À propos du cours

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....

Meilleurs avis

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 sur 497 Examens pour Apprentissage mechanique pratique

par Xray W

Mar 21, 2016

Principle and practices. Good coverage on topics to get you started!

par Abhishek S

Aug 15, 2017

Excellent course.

par PATRICK

Mar 02, 2017

Nice, clear and concise.

par Roberto D

Jun 20, 2017

Methods to be applied in preparation for creating a data product.

par Prohnițchi V

Dec 31, 2017

Great course. A lot of extremely useful stuff.

par antonio q

Feb 27, 2018

it was great, simply though exhaustive, thanks a lot

par David Y

Feb 09, 2016

Enjoyed without reservation

par Peter T

Feb 29, 2016

Love it.

par Kristin A

Jan 09, 2018

Good intro to a topic that has a lot of power and a rich body of knowledge behind it. You can only scratch the surface in a four-week course, but I have been exposed to quite a range of tools in Practical Machine Learning.

par Avirup N

Mar 07, 2016

Very informative

par Fernando S e S

Jul 24, 2016

It's hard as hell, and very good.

par Sebastian F

Jan 24, 2016

Great course. Really educational and informative. Well taught too!

par Rudolph A M

Oct 21, 2016

Wonderful!

par Arunkumar M R

Sep 30, 2017

Awesome course. Super effective quizzes.

par Jay S

Aug 27, 2016

Excellent introductory course to Machine Learning. Very informative materials. Prof. Leek is a great teacher.

par Jorge M A A

Apr 13, 2016

I enjoyed a lot this module, I'll use at my daily work some of the features I learned

par Edward R

Dec 17, 2017

Great course, but it may take you more than the allotted 4 weeks if you intend to dig a bit deeper and pursue some of the additional resources referenced throughout the course. I would definitely recommend doing that, as there is A LOT of material to cover if you, like me, just have to know the details of what's happening behind the scenes.

par Monnappa

Nov 12, 2016

Good content as an introduction to Machine learning!

par Donson Y

Sep 04, 2017

This is a fantasy course to know that how to build your first machine learning model.

par Bill K

Feb 10, 2016

Really good class. I think there were some small issues with the class project. Like all real world problems it was not entirely well specified and the data was a bit odd to use for a prediction exercise because it was time series data.

par Jose R C

Aug 16, 2016

The machine learning course every Data Scientist should do.

par Diana S

Feb 11, 2016

Thank you son much!!!!

I really like the course.

It help me in my job =)

par Saul L

Feb 08, 2016

This is by far the most enlightening class in the whole specialization. I really got a good handle about how to build a predictive model and apply it to real datasets.

par Rodney A J

Jul 30, 2017

This was a great course.

par Francisco J D d S F G

Nov 27, 2016

The best course of the specialization along with the statistical inference one - the final assignment is very fun to do, pretty much like a Kaggle competition.