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

4.5
2,675 notes
501 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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76 - 100 sur 492 Examens pour Apprentissage mechanique pratique

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 KOALA V

Sep 25, 2017

Very interesting course

par Rui R

Feb 06, 2017

One of the best courses in the Data Science Specialization,

par Emanuele M

Nov 15, 2016

It very well done, good pace, and gives you real and concrete elements and examples to build a fully functional machine learning algorithm! i recommend this course

par Piotr K

Oct 23, 2016

Nice introduction to machine learning in R. It is rather basic level, so it not for people that already know some basics related to regression and classification.

par Jeremy O

Mar 10, 2017

excellent!

par Light0617

Sep 04, 2016

great!!! In this lecture, I learn how to write R code to analyze data with Machine learning methods.

par Divvya.T

Oct 29, 2017

Good course to take !!

par Shivanand R K

Jun 21, 2016

Great and Excellent thoughts and course material.

par Do H L

Mar 10, 2016

Useful course that is very practical in teaching tools in R that enable Machine learning. This course is, however, not suitable for people who want to learn theoretical machine learning. For that, learners will find Machine Learning by Andrew Ng a better alternative. However, if you're interested in machine learning packages in R and how to implement them, this course achieves that purpose for you.

par aditya n p

May 12, 2016

Awesome Course !!

par Angel D

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.

par Forest W

Jan 09, 2018

Much Better than the previous courses ( Regression and Statistical Inference)

par Nirav D

Apr 02, 2016

This is a very useful course in Machine Learning that teaches us how to use the R based packages such as CARET for applying machine learning techniques. The course project helps understand how these techniques are applied in real world applications and develop useful insights.

par Supharerk T

Mar 07, 2016

I want to learn ML in R so I go straight to this course without taking any other course in this specialization, and it doesn't disappoint me. Thanks for a great course!

par Florian

Jul 09, 2016

Great primer for machine learning with ample additional resources for those who are interested. I feel this course gave me a solid basis to delve deeper into the topic.

par Sebastian F

Jan 24, 2016

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

par Rudolph A M

Oct 21, 2016

Wonderful!

par PRAKASH J M

Dec 25, 2017

Pushed me to learn and experiment and make mistakes. Thank you

par David S

Feb 07, 2016

The course gives a clear explanation of why machine learning, with a goal of prediction, is different from regression. The use of the caret package in R is emphasized. Caret provides a uniform interface to many different machine learning algorithms, leaving no excuse for practitioners not to test a variety of approaches to confirm the robustness of their conclusions.

par Robert K

Sep 26, 2017

A great introduction to machine learning and it does a good job building on the material from the previous classes.

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