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Kursteilnehmer-Bewertung und -Feedback für Practical Machine Learning von Johns Hopkins University

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2,973 Bewertungen
566 Bewertungen

Über den Kurs

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....

Top-Bewertungen

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

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.

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476 - 500 von 558 Bewertungen für Practical Machine Learning

von Andrew W

Feb 10, 2017

The videos are really tutorials on R functions for machine learning and data wrangling. A good substitute for "Machine Learning" by Andrew Ng in terms of managing data sets and exploratory analysis.

von M. D

Jul 11, 2020

Content somewhat outdated. Referenced packages don't always work in current version of R. Material can be better explained with more detailed discussion of examples rather than theory.

von Robert C

Aug 01, 2017

This course needs swirl assignments. I did fine on the quizzes and assignments, but I only feel like I learned a minimal amount of machine learning, even practical machine learning.

von Raul M

Feb 12, 2019

The class is good but it is too simple. I expected the professor will provide more detail about the models. This is just an introduction and weak for a specialization.

von Brian F

Aug 16, 2017

There was some good material in here, but it was rushed and is deserving of a much longer course - especially compared to some of the other modules in this course.

von Chuxing C

Feb 05, 2016

the lack of assisted practices made it harder to digest the contents and methodologies.

strongly suggest to develop some practice problems with explanations.

von Michalis F

May 26, 2017

Good in introducing caret package and getting some experience in running algorithms. Was expecting more in-depth discussion about the methods though.

von Davin G

Aug 26, 2019

It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.

von Léa F

Jan 09, 2018

Rather good overview. The contents could dig deeper into each subject, and it would improve the course a lot if some exercises in Swirl were added.

von Miguel J d S P

May 19, 2017

I didn't enjoy the supporting materials and the quizzes weren't very interesting. The final project was fine.

The subject is super interesting.

von Max M

Dec 12, 2017

Should have gone into more depth and included swirl lessons, like previous courses. The quizzes were very challenging though, so that helped.

von Kyle H

May 09, 2018

A brisk introduction to some of the basics of Machine Learning. Will leave with an understanding of a few ways to use the caret package.

von Manuel E

Aug 08, 2019

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

von Noelia O F

Jul 19, 2016

Good course for learning the basics of the caret package. However, it is not a good course for learning machine learning.

von Joseph I

Feb 01, 2020

Material was very interesting but was covered at a very high level and a lot of additional learning was required.

von José A G R

Feb 05, 2017

Superfluous but the existence of the package "caret" covers the gap of other libraries like "skilearn" of python

von BAUYRJAN J

Mar 01, 2017

Instructor rushes the course and does not explain much in the same level of details as respective quiz requires

von Hongzhi Z

Jan 03, 2018

All the formulas and code in slides are too abstract. If can be more charts to interpret that will be better.

von Henrique C A

Oct 14, 2016

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

von Alex F

Dec 30, 2018

A fine introduction, but there are much more engaging and better quality courses out there...

von Yingnan X

Feb 11, 2016

If you have taken Andrew Ng's machine learning class, it's not necessary to take this one.

von Yohan A H

Sep 06, 2019

I think it was a very fast course and I feel more real examples would have been useful,

von fabio a a l l

Nov 14, 2017

Poor supporting material in a course that tries to cover a lot in a very limited time.

von Rafael S

Jul 24, 2018

this course seemed too rushed for me, too little content for such a extense subject

von Raj V J

Jan 24, 2016

more needs to be taught in class. what is taught is not sufficient for quizzes.