Chevron Left
Retour à Apprentissage mechanique pratique

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

4.5
2,667 notes
500 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.

Filtrer par :

101 - 125 sur 491 Examens pour Apprentissage mechanique pratique

par Ivan Y

Mar 06, 2018

great intro to machine learning!

par sampath

Oct 13, 2017

Tough but very good course

par Raju G

Nov 26, 2017

Extremely useful.

par Evgeniy Z

Apr 12, 2016

Nice course.

par Douglas M

Feb 01, 2016

Great practical whirlwind tour. Light on theory, however, but it's a good entry point to the field. Thumbs up!

par Wesley E

Feb 15, 2016

Great introduction with a broad set of tools and plenty of resources for more in depth study.

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 Jeru

Nov 23, 2016

Beautiful insight into ML

par Nathan M

Jun 11, 2016

Extremely useful class! Jeff also has many excellent suggestions for resources that will teach you even more about machine learning.

par Ozanb

Apr 21, 2017

Best course of the programme. Simple and explained well.

par Araks S

Aug 31, 2017

Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.

par Daryl V D

May 31, 2017

Fantastic Class!

par Chinmoy D

Sep 10, 2017

very nice course, and quite informative too

par Camilo Y

Mar 14, 2017

This course is a good introduction to machine learning algorithms with R

par Dan K H

Mar 27, 2017

Yet again an excellent course by Jeff, Roger and Brian. Thank you very much for a well layout course and some good excersizes.

par Lei M

Aug 23, 2017

This course is demanding, but I feel my own progress which is very fulfilling.

par UDBODH

May 14, 2016

Nice learning course

par Stephanie D

May 21, 2017

This was definitely a challenging course. I learned a lot about building and testing prediction algorithms. The course also helped me overcome the feeling of intimidation by providing excellent examples and a hands-on final assignment.

par Alejandro B G

Feb 09, 2016

A great Course, my favorite into the Data Science Specialization

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.