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

4.5
étoiles
2,918 évaluations
554 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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376 - 400 sur 545 Avis pour Apprentissage mechanique pratique

par Kevin S

Mar 03, 2016

Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.

par Sulan L

Nov 19, 2018

I hope we can have more détails in this cours and to see how to use the algorithms for the big data. Thank you.

par A. R C

Oct 20, 2017

I enjoyed it but it needs indeed to deep into many concepts, which are just briefly named during the course.

par marcelo G

Aug 15, 2016

Great course, very demanding, but it could use more reading material, ebooks instead of links on video.

par Jeffrey E T

Mar 28, 2016

Good overview of available techniques and the Caret package. Will get you started in machine learning.

par BIBHUTI B P

Jul 24, 2017

This was a superb module which created a deep learning insight within me focusing on future technology

par João R

Aug 20, 2017

Got confused how to perform cross validation and when. Other than that, very practical. Great job.

par Daniel R

May 14, 2016

The course is really great, however it should last a little longer, 4 weeks is hard to accomplish

par César A C

Jul 26, 2018

Very interesting course. May be a little bit harder than the previous ones but it could be done.

par Greig R

Nov 14, 2017

Good course, I learnt a lot. It does need to be updated with more modern versions of software.

par Pieter v d V

Jun 28, 2018

Very quick overview. If you really want to know something about it read the reference books.

par Guilherme C

May 18, 2016

Title says everything. Practically and basically no theory explained. Good course though.

par Carlos C

Aug 12, 2017

Excellent content so I give 4 starts. I stat less because the trainer speaks too fast.

par carlos j m r

Oct 05, 2017

I thought there were Swirl practice as other courses, however this course is very good.

par alon c

Mar 10, 2016

Great Course, will be nice to have more projects to see how it goes with different data

par Anant S

Jun 30, 2017

good course for initial understanding of machine learning. SVM can also be included.

par Caio H

Aug 23, 2019

I learned a lot in this course, but I would recommend taking the courses in order.

par danxu

Mar 14, 2017

very good, but if it has swirl practice like th other courses it would be perfect.

par Christian W

Jan 31, 2017

First 3 weeks are manageable and the final project is great! I had a lot of fun :)

par Yew C C

Feb 04, 2016

Wish to have more systematic structure, detail information and hands-on exercises.

par vivek s

Jun 07, 2016

introduces lot of machine learning techniques which are used by practitioners !

par Ramiro A

Aug 31, 2016

Nice course, Gives a god insight on what can me done with R and Predictions

par Daniel U

Feb 17, 2016

Fast paced and little focused on the algorithms but quite useful overall.

par Matthew L

Jan 06, 2016

Really good overview of machine learning techniques and model evaluation.

par bhawani p

Jan 07, 2017

briefly summarised the machine learning algorithms. Good place to start!