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

4.5
étoiles
2,769 évaluations
519 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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326 - 350 sur 510 Avis pour Apprentissage mechanique pratique

par Robert W S

Nov 22, 2016

Great intro to machine learning. Several algorithms with some ideas on sampling and pre-processing techniques are covered. Adding a textbook as done with some of the other data science classes would help, but other resources are referenced.

par Sabawoon S

Sep 14, 2017

Excellent course, very practical. Found the project challenging as preprocessing data required some knowledge of the limitation of the RandomForest method i.e. both train and test needs to have same classes of data with similar levels.

par Kalle H

Jun 25, 2018

Nice course that tries to fit a lot of material into four weeks. Due to this, the material is not so deep, although pointers are given to where the student can find additional information related to each subject covered by the course.

par Kamran H

Feb 18, 2016

Pretty good overview of how to build some types of machine learning models through the caret library in R, but not much in terms of the theoretical underpinnings or why one method is better than the other or where it is most suitable.

par Brynjólfur G J

Sep 25, 2017

Some problems with current and old versions of packages and problems with using other packages on different operating systems. Though that did also help foster an independent research style which will help me in the future.

par Chonlatit P

Oct 20, 2018

GREAT course! There are all base of machine learning field. The limitation is blur between basic and detail especially maths. This course, sometimes , show the maths that make you confuse if you're not familiar with them.

par Emily M

Mar 12, 2018

This course gives an overview of a broad subject. My personal feeling is that there could have been some more indepth examples/case studies to demonstrate how to apply these methods and analyse /interpret the outcomes.

par Orest

Jan 22, 2018

It needs more mathematical detail. Otherwise is a fairly comprehensive class, and a great tutorial on the caret package. I recommend it, if you need to refresh concepts and get some practical exposure to caret.

par Bruce I K

Oct 20, 2016

It's a great course but I hope you add a few things. The course about the machine learning algorithm is so basic. Please get deep into the machine learning algorithm. Then it would become the perfect course.

par Aashaya M

May 29, 2016

In my opinion this course is highly technical and demanding in nature compared with the others. The learning experience is good and coursera.org has given a opportunity for customization ! thank you Coursera

par Paul K

Apr 08, 2017

Very good summary of strengths/weaknesses of various machine learning algorithms. This lecturer's style and production quality is much higher than in the previous two courses in the specialization series.

par Erika G

Jul 28, 2016

I learned a lot in this class. There are slight gaps from the depth of material covered in the lectures to the quizzes and assignment. If you're good at researching online, you'll be fine.

par Jiarui Q

Mar 27, 2019

It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.

par Matthew C

Dec 11, 2017

Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.

par Utkarsh Y

Nov 17, 2016

Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.

par Craig S

Feb 12, 2018

Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

par Roberto G

May 21, 2017

Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice

par Eric L

Jun 02, 2016

Great course, very high paced with a lot of information. would have been great to add two more weeks and another project to use more machine learning

par Igor H

Sep 10, 2016

Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.

par Lee G

Sep 22, 2017

A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.

par Ann B

Sep 06, 2017

Good class to get the basics of Practical Machine Learning. This course is best taken as a part of the data science series from John Hopkins.

par Hernan S

Dec 13, 2016

The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.

par Jakub W

Sep 24, 2018

Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice

par Md F A

Aug 14, 2017

To me with this course, the best learning aspect is the final project; how to use Machine Learning Algorithms on data analysis.

par Rhys T

Oct 10, 2017

Good course, some aspects of the assignment were a bit beyond the scope of what the course teaches but overall I learnt a lot.