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

4.5
étoiles
2,984 évaluations
569 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

JC

Jan 17, 2017

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!

MR

Aug 14, 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

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301 - 325 sur 560 Avis pour Apprentissage mechanique pratique

par Sumeet M

Jan 08, 2018

Nice Course

par Javier E S

Dec 02, 2018

Excellent.

par Jeremy O

Mar 10, 2017

excellent!

par Rudolph A M

Oct 21, 2016

Wonderful!

par Neven S

Jan 22, 2016

Very good!

par Luis M M R

Dec 24, 2018

very good

par Carlo G I

Dec 04, 2018

thank you

par gerson d o

Dec 25, 2019

Perfect!

par Fernando L B d M

Oct 22, 2017

Awesome!

par Fábio A C

Dec 25, 2016

The best

par Artem A

Apr 14, 2016

Noiiice!

par Peter T

Feb 29, 2016

Love it.

par Johan J

Nov 21, 2016

Awesome

par Pedro M

Jan 11, 2020

Great!

par George O O

May 08, 2018

Great!

par Md. R Q S

Sep 18, 2020

great

par Sai P G

Sep 07, 2017

good

par Khairul I K

May 27, 2017

Good

par Yi-Yang L

May 19, 2017

Good

par Larry G

Feb 07, 2017

Nice

par Kidpea L

Oct 04, 2018

tx

par Amit K R

Nov 21, 2017

ok

par Reinhard S

May 19, 2017

ok

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 Robert O

Jul 27, 2017

The course subject matter was great but like the course 6 & 7 scenarios i found the lectures didn't reiterate or reinforce key takeaways that are easily confused. For example is cross validation when you split the data into a training and testing, when you have a separate unknown results set to test final training model on. Or does it require doing folds and then breaking each of those up into training and testing chunks. Or why is it not okay to use a model training function that internally does cross validation similar like randomForest documentation suggests. Also things like what the prediction accuracy implies in contrast to the model oob [ in ] sample error estimate and if that estimate is akin to the 1 - prediction accuracy on test data set, i.e. out of sample error estimate. Seems like liitle coverage was given to whether or not there are well known training models to use or if you literally need to try and compare the 1/2 dozen or so common ones out there every time to find out which one to use for a given dataset. Also left confused about overlapping use of words classification model training, i.e. are they synonyms for the machine learning based functions we use to try and fit models to data.