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學生對 约翰霍普金斯大学 提供的 实用机器学习 的評價和反饋

4.5
3,062 個評分
580 條評論

課程概述

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....

熱門審閱

MR
2020年8月13日

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
2017年2月28日

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.

篩選依據:

501 - 实用机器学习 的 525 個評論(共 571 個)

創建者 Kyle H

2018年5月9日

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.

創建者 Manuel E

2019年8月8日

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

創建者 Noelia O F

2016年7月19日

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

創建者 Joseph I

2020年2月1日

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

創建者 José A G R

2017年2月5日

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

創建者 BAUYRJAN J

2017年3月1日

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

創建者 Hongzhi Z

2018年1月2日

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

創建者 Henrique C A

2016年10月13日

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

創建者 Alex F

2018年12月29日

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

創建者 Yingnan X

2016年2月11日

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

創建者 Yohan A H

2019年9月6日

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

創建者 fabio a a l l

2017年11月14日

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

創建者 Rafael S

2018年7月24日

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

創建者 Raj V J

2016年1月24日

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

創建者 Surjya N P

2017年7月2日

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

創建者 王也

2016年12月17日

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

創建者 james

2016年9月10日

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

創建者 Philip A

2017年2月26日

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

創建者 Christoph G

2016年12月4日

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

創建者 Ariel S G

2017年6月27日

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

創建者 Jorge L

2016年10月13日

Fair but assignments are not very well explained

創建者 Bahaa A

2016年10月19日

Good enough to open up mind of researcher

創建者 Johnnery A

2020年3月20日

I need study more this course

創建者 Sergio R

2017年9月20日

I miss Swirl

創建者 Serene S

2016年4月28日

too easy