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

3,050 évaluations
579 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

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

28 févr. 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.

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401 - 425 sur 570 Avis pour Apprentissage mechanique pratique


24 juil. 2017

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

par João R

20 août 2017

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

par Daniel R

14 mai 2016

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

par César A C

26 juil. 2018

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

par Greig R

13 nov. 2017

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

par Pieter v d V

28 juin 2018

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

par Guilherme C

18 mai 2016

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

par Carlos C

12 août 2017

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

par carlos j m r

5 oct. 2017

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

par alon c

10 mars 2016

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

par anant s

30 juin 2017

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

par Caio H

23 août 2019

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

par danxu

13 mars 2017

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

par Christian W

31 janv. 2017

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

par Yew C C

4 févr. 2016

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

par vivek s

7 juin 2016

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

par Ramiro A

31 août 2016

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

par Daniel U

17 févr. 2016

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

par Matthew L

6 janv. 2016

Really good overview of machine learning techniques and model evaluation.

par Bhawani P

6 janv. 2017

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

par S M H R

10 févr. 2016

A good course where you can learn how ML algorithms work practically.

par Saurabh K

9 mars 2017

Very useful course to develop level knowledge in machine learning.

par Johnny C

23 oct. 2018

It was in general nice course. However, quizzes need improvement.

par Karthik R

7 août 2017

Bit tough, but I will have to say, good introductory course.

par Coral P

18 août 2017

The project is good in letting us practise what we learnt