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

4.5
2,570 notes
480 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

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.

DH

Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

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226 - 250 sur 472 Examens pour Apprentissage mechanique pratique

par Robert H

Apr 14, 2017

Really hands-on compact introduction!

par Nikhilesh J

Mar 02, 2018

Provides a quick and dirty look at Machine Learning. An easy way to get started.

par Pam M

May 19, 2016

Good material, presented in an organized fashion. I was able to apply what I learned immediately in a work setting.

par Giovanni M C V

Feb 16, 2016

Excellent course with great didactic. Congratulations!

par Fernando M

Sep 04, 2017

Great material. Really enjoyed it

par VENKATESH G S

Oct 30, 2017

Good Approach......Valuable Course......!!!

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 chris

Sep 20, 2017

piece de resistance

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 Bojan B

Oct 14, 2018

Great course with great materials. Easy to understand and to learn.

par Shashwat K

Oct 15, 2018

insightfull

par Yusuf E

Oct 17, 2018

It would have been nice if there was an introduction to deep learning. Also, linear methods are discussed at length again which is not really necessary. Otherwise, great course to get you started on machine learning applications in R.

par Ted

Oct 18, 2018

Greate course! Lot of things learned!

par Pablo L

Sep 20, 2018

Excelent course, it's a little bit short considering the breadth of the topic, but covers the most important algorithms and never abandon it's focus on methodology.

par Rebecca K

Sep 24, 2018

This course gave a great basic understanding of some different machine learning algorithms and what they do. I now have a great practical understanding of how to implement them, and enough understanding of theory to know what I'm talking about and to be able to learn more about them in the future.

par Harland H

Oct 01, 2018

Very informative and the project was fun to accomplish.

par Daniiar B

Sep 29, 2018

It lucks theory, but that's why it's called practical. Very hands on teaching method. Was a little bit hard to follow.