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学生对 约翰霍普金斯大学 提供的 实用机器学习 的评价和反馈

4.5
3,058 个评分
579 条评论

课程概述

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....

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MR
Aug 13, 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
Feb 28, 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.

筛选依据:

501 - 实用机器学习 的 525 个评论(共 570 个)

创建者 Manuel E

Aug 8, 2019

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

创建者 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.

创建者 Joseph I

Feb 1, 2020

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

创建者 José A G R

Feb 5, 2017

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

创建者 BAUYRJAN J

Mar 1, 2017

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

创建者 Hongzhi Z

Jan 2, 2018

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

创建者 Henrique C A

Oct 13, 2016

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

创建者 Alex F

Dec 29, 2018

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

创建者 Yingnan X

Feb 11, 2016

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

创建者 Yohan A H

Sep 6, 2019

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

创建者 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.

创建者 Rafael S

Jul 24, 2018

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

创建者 Raj V J

Jan 24, 2016

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

创建者 Surjya N P

Jul 2, 2017

Overally course is good. But weekly programming assignments will be great.

创建者 王也

Dec 17, 2016

Too different for beginners but not deep enough for ones already know R.

创建者 james

Sep 10, 2016

Quizzes are useful exercises but need to do a lot of self studying.

创建者 Philip A

Feb 26, 2017

mentorship was great, but the video lectures were almost useless.

创建者 Christoph G

Dec 4, 2016

The topic is too big, for one course from my point of view.

创建者 Ariel S G

Jun 27, 2017

In my opinion, this course needs a few extra exercises.

创建者 Jorge L

Oct 13, 2016

Fair but assignments are not very well explained

创建者 Bahaa A

Oct 20, 2016

Good enough to open up mind of researcher

创建者 Johnnery A

Mar 20, 2020

I need study more this course

创建者 Sergio R

Sep 20, 2017

I miss Swirl

创建者 Serene S

Apr 29, 2016

too easy

创建者 Estrella P

Jul 7, 2020

.