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Avis et commentaires pour d'étudiants pour Apprentissage mechanique pratique par Université Johns-Hopkins

4.5
étoiles
3,196 évaluations
615 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

16 janv. 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!

MR

13 août 2020

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

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601 - 606 sur 606 Avis pour Apprentissage mechanique pratique

par Abhilash R N

4 déc. 2019

This course is NOT for the beginner. Take time to finish all the beginner and foundation courses and then take time to learn R

par Yesica B

29 déc. 2021

I wanna know, what is happening with my grade with this course. I still wait long time ago. Please, help me.

par Emily S A

25 mai 2020

In my opiion, this course needs to be improved a lot. There are almost nothing Practical Machine Learning.

par yi s

19 juil. 2016

too general no depth, not recommended for science or engineering degree holders

par Stephen E

27 juin 2016

To be honest I don't think this is worth the money.

par Stephane T

31 janv. 2016

Too much surface, not enough depth.