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

4.5
stars
2,749 évaluations
514 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.

AS

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.

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76 - 100 sur 505 Avis pour Apprentissage mechanique pratique

par Selim J R

Dec 15, 2016

Excellent course. I feel like i know so much already even though we scratched the tip of the iceberg. Will definitely enroll in more advanced courses.

par Pouria T

Jul 02, 2017

Great course, thank you. I was able to use what I have learned from the previous 7 courses and see them on in action through this course. Thank you :)

par Sinan G

May 29, 2017

Very fine course in machine learning where the focus is more on the use of ML rather on the theory behind it i.e. the course title fits its contents.

par Claudio F S

Dec 01, 2019

The course is amazing. The use of training and testing to predict data analysis made me more fascinated and interested in Data Science. Very nice!

par Jerome S P

Jun 18, 2019

Very good explanation! Trying to do the examples help me understand more plus the explanation which is not on the slide helps a lot. Thank you

par Shashikesh M

Aug 03, 2017

An absolute approach of learning machine learning in very unique practical manner. Fundamental at the same to very practical learning course.

par Chris N

Jun 07, 2017

loved it - fascinating subject and more detail than you could possibly want from the course instructors. Friendly community in the forum too.

par Matthew W

Mar 01, 2016

High level and brief overview but found it informative introduction into machine learning with R. The final project is fun and interesting.

par Javier A D

May 27, 2018

References were very usefull for doing deep analisys in the thems

Quices were challenge.I learn a lot solving them.

I mis the swirl sessions

par Laro N P

Jul 22, 2018

Good course, I miss more practice exercise because theory is always welcome but when we are capable to understand is doing real practice.

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 Diandian Y

Nov 28, 2019

a broad coverage of content and very intuitive explanation for different algorithm. Good start point to learn machine learning.

par Avizit C A

Jan 31, 2019

A very good course giving brief descriptions and applications of some of the used statistical and machine learning algorithms.

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 Peter D

Oct 07, 2016

One of my favorites in the series! What I have been waiting for building up the prerequisite knowledge. Enjoy the instructor!

par andy p

Aug 10, 2016

Great topic with a great instructor. Only wish the program was a little longer to spend some more time on some of the models.

par Prakhar P

Jun 06, 2018

This course introduces to the machine learning package caret. A solid launch pad into the exciting world of data analytics.

par Moises E

Jan 16, 2017

Very good overview and straightforward explanations of the different methodologies of ML. Nice tips on how to do ML with R.

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.

par Sabitabrata M

Jun 10, 2018

Good course. Good overview on Machine Learning. But to understand the concepts I had to consult external resources.

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 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 Rahimullah S

Oct 28, 2018

thank you, this class is very practical and informative. The projects are a little complicated but very practical.

par BOUZENNOUNE Z E

Dec 18, 2019

A Great course that should be taken along other books, tutorials, and papers, in order to get the most out of it.

par Matthew S

May 08, 2019

Good introduction to machine learning. Provides pretty comprehensive coverage of major algorithms and approaches.