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

4.5
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3,200 évaluations

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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151 - 175 sur 607 Avis pour Apprentissage mechanique pratique

par Enrique A M

18 oct. 2020

Mil Gracias Maestro Roger y demas docentes, Mil gracias U. John Hopkins, Mil Gracias Coursera.

par Gustavo C G

7 août 2019

Excellent introduction to machine learning. Great examples and detailed explanations, as usual

par Luis F T R

11 août 2022

Amazing course, it covers machine learning basics and popular algorithms and their use cases.

par Thodoris M

10 juil. 2018

Practical ML is a great course, that provides training in the practical aspects of the topic.

par Wesley E

15 févr. 2016

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

par André C L

13 déc. 2018

very good practical experience using machine learning models, especially regarding PCA usage

par Raunak S

19 nov. 2018

a very good course for those wanting to learn Machine Learning to implement in Data Science.

par Tristan F

25 déc. 2019

Lectures were very clear and helpful! Professor Leek was great at breaking down the topics.

par Oleksandr K

11 juil. 2018

Great course! However, it would be good to learn about artificial neural networks as well.

par Jean N

24 août 2017

Very nice Course. I am applying it right away for Predictions in the Telecoms environment.

par Tomer E

6 août 2020

Great course!

Covers basics of machine learning algorithms and how to implement them in R.

par Rizwan M

13 oct. 2019

great course. could have explained more techniques in caret package with coding examples

par Connor B

24 sept. 2019

Really good exposure to machine learning and builds on the previous course in regression

par Alfonso R R

13 nov. 2018

Hands on course. Loved it. It goes a little bit fast, however, the content is ambitious.

par Brian G

17 août 2017

Great course. Mechanics of the final assignment are more difficult than the work itself.

par Paresh P

8 déc. 2020

Explained practical machine learning well, concepts like model stacking really helped!

par Sean D

10 juin 2020

Really liked Dr. Leek's talks, and the subject matter was interesting and kind of fun.

par Konstantin

2 mars 2020

Excellent course. Lots of exorbitantly useful knowledge. I`ve been lucky to start it.

par Donson Y

4 sept. 2017

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

par Jorge A

13 avr. 2016

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

par Premkumar S

16 mars 2019

Great course and farily challenging exercises! Thank You for putting this together!!

par Sai S

17 juil. 2017

Great course. Ways to curb plagiarism & cheating needs to be revisited by your team.

par Thet P S A

21 août 2020

It supports a lot in my thesis. Thank you, lecturers, at John Hopkins University.

par Mary

19 août 2019

Very informational with good variety of code to take back and apply to projects.

par Nikhilesh J

2 mars 2018

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