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Avis et commentaires pour l'étudiant pour Apprentissage mechanique pratique par Université Johns-Hopkins

2,639 notes
498 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


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.


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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276 - 300 sur 489 Examens pour Apprentissage mechanique pratique

par Connor B

Sep 24, 2019

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

par Weiqun T

Sep 23, 2019

This is a very good basic course for machine learning. I got the basic ideas and skills for it.

par Ben H

Oct 07, 2019

Really nice introduction to machine learning in R. You wouldn't want to pack more than this in 4 weeks. Would be interested to see if this course adopts the recipes / parsnip / tidymodels in the future.

par Ashwin V

Oct 11, 2019

Best course

par Rizwan M

Oct 13, 2019

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

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 Diego T B

Nov 07, 2018

Very useful. The models were very easy to understand

par Terry L J

Nov 09, 2018

Lot of good material, however, on all of these courses, it would be very helpful if they were better organized for learning.

Overview of learning objectives in a step sequence for a more organized approach for learning (maybe even a process roadmap map sequencing activity to follow that you can reference back to.

Detailed information for each step in the learning process that can be followed that maps back to the roadmap.

A summary of the learning objective in the roadmap sequence.

Basically, just like writing a paper, > overview/objectives > Main topics >subtopics, etc. > summary

par Sakib S

Mar 15, 2019

Include more swirl practice problems.

par Shobhit K T

Dec 18, 2017

Great course with practical insights on machine learning

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 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 Manuel C

Dec 26, 2017

I feel I could have master the subjects better

par Moiz

Feb 03, 2017

By using the caret package, this course took a very pragmatic approach towards machine learning. It demonstrated how to perform all the essential tasks in making the machine (algorithm) learn from data.

In my case, this course required a dedicated time commitment for successful completion. In addition to course lectures, i used the 'Machine Learning with R' book to fill my knowledge gaps. Overall i feels that this course helped me in my journey of gaining a better understanding of this subject.

par Vathy M K

Aug 13, 2016

It's very cookbook driven - it's not a deep dive into the topics. This can be dangerous: a little knowledge and all that. However references for more are provided. If you can imitate the coding examples, you should be OK for the assignments. Fair warning: the quizzes are hard to replicate unless you set up your environment to mirror exactly the version of the packages used in the course.

par Rudolf N

Dec 19, 2015

Thank you for inviting me to be a beta tester for Practical Machine Learning. I completed this course at the beginning of October of this year. When I was asked to be a "beta tester" I thought that I would be presented with new materials. However, the only thing that has changed is the look and layout of the Coursera web pages. The video lectures, quizzes, and assignment are the same as they have been for quite some time. Here are some specific comments:

1. The video lectures: To me, these are clear and easy to follow. However, like those in the other courses in the Data Science Specialization, this course covers a wide range of subjects but tends not to have much depth. When I compare this and other courses in the specialization to other moocs that I have taken including Machine Learning with Andrew Ng and the Stanford Online EdX Course Statistical Learning with Trevor Hastie and Rob Tibshirani, the somewhat cursory treatment of the topics in the Data Science Specialization becomes more noticeable. Perhaps in the interest of "truth in advertising" this course should be called "A Brief Introduction to Practical Machine Learning." In the interest of full disclosure, I should note that I have an undergraduate degree in economics and an MS and PhD in psychology with a quantitative bent. I have had lots of statistics courses, especially those related to ANOVA, MANOVA, nonparametric statistics, correlation and regression methods, and structural equation modeling. The latter is important in psychology because researchers in this field like to measure latent variables. I had been an analyst using SPSS for several decades and the courses in this specialization helped me to migrate to R. Also, there have been may new developments that have become more accessible through R packages (like the fancier tree methods) that were not available when I completed my PhD. Thus these courses (and others such as the ones by Ng and Hastie and Tibshirani) have helped me to keep abreast of these developments. So they are good for me, but I wonder to what degree do the courses in the Data Science Specialization actually make a person a "data scientist?"

2. The quizzes: I think these items are good practice and are at a reasonable level of difficulty. However, these items are the same ones that you have been giving for quite some time, with perhaps a few new ones added. A little googling will lead you to the answers to these quizzes posted online. I recommend that you put a little time and effort into writing all new items.

3. The final project: Again, this project is good practice and seems to be at a reasonable level of difficulty. And again, this is the same project that appears to have been given at the end of numerous iterations of this course. And again, numerous write-ups for this project can be found online. And again, I would recommend that you put a little time and effort into finding a new data set for people to analyze. This would help minimize some of the rampant cheating that I found in this and in other classes in the specialization.

On the subject of cheating, when I was doing the peer grading for the courses in the Specialization, I would enter the code of the students that I was grading into the Google search box and all too often I found links to submissions for the project by students who had taken earlier sessions of the class. That is, students were copying these earlier submissions by other students and submitting them as their own. And I don't mean that they were similar: students were copying other people's work line by line, character by character. I found that to be quite irritating and I always reported it to Coursera. Of course, if the instructors would change their assignments once I a while, then this sort of copying would be impossible. As it is, it appears that the good professors put a lot of time and effort into creating what are indeed a worthwhile set of classes. However, after they created the classes, they seem to have pushed the "autopilot" button and gone off to do their day jobs. I would suggest that re-engaging with these courses and reading some of the comments that other students have made would be helpful.

Overall, I appreciate the courses in the Data Science Specialization and specifically this course. I know that these class materials took considerable time and efforts to create. I wish the instructors continued success with these classes.

par vivek s

Jun 07, 2016

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

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 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 Sean Q Z

Dec 11, 2016

As the title states, very practical way to show you how this is done in R.

Most of them are lines of codes and some explanation. There are tons of details behind that and remains un-explained.

As other courses in the specialization, students need to do a lot of self-study to further understand machine learning.

But at least, learned a lot.


Jul 24, 2017

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

par Coral P

Aug 19, 2017

The project is good in letting us practise what we learnt

par Mehul P

Oct 03, 2017

Good ML overview.

par Stephan H

Aug 12, 2017

Very challenging course. I learned a lot. Tanks.

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.