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学生对 密歇根大学 提供的 Applied Machine Learning in Python 的评价和反馈

4.6
6,235 个评分
1,119 条评论

课程概述

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

热门审阅

OA

Sep 09, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

FL

Oct 14, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

筛选依据:

901 - Applied Machine Learning in Python 的 925 个评论(共 1,101 个)

创建者 Christian P

Aug 05, 2019

Code and examples were very useful. Teaching a bit lengthy and detailed at times. Overall a very good course for getting hands-on machine learning in python.

创建者 Weiqi Y

Oct 25, 2017

It's alright as a course focusing on applied techniques. If you are expecting more theories and understanding of the algorithms, this one may not for you

创建者 Helen L

Jun 15, 2020

Submission isnt easy often gave errors that are not due to students' faults. Time-consuming unnecessarily. The content and assignments are great.

创建者 Utkarsh S

Jun 22, 2020

Very informative course, the only issue I had was with the file locations in the assignments. Takes up a lot of time switching back and forth.

创建者 Mariano T

May 19, 2020

There are some problems with the assignments but the course is very good. You must improve the material for the assiggnment. I love the forum

创建者 Srinivas K R

Sep 22, 2017

Good overview of machine learning topics with practical exercises in the use of multiple techniques primarily through use of scikit-learn.

创建者 David W

Jul 03, 2017

Hands on and practical. Dr. CT and his staff have done a great job introducing Machine Learning. Where were you 20 years ago? Thank you!

创建者 Rakshit T

Jul 10, 2018

A good course for beginners in Machine Learning. You get to the learn the basics of many techniques and their implementation in python.

创建者 yannick t

Apr 12, 2018

Excellent lectures. However, I would have needed more guidance for the last assignment. I learned a lot, but through pain and struggle.

创建者 Prathmesh D

Jul 15, 2020

It was a great learning with you all got little problems but solved as per instructions and they helped me through that,thanking you

创建者 Dr. P R K

Jan 23, 2018

Unlike the name suggests, this course only covers the Supervised learning side of the ML. However, the supervised side is good.

创建者 Michael S

Jun 29, 2019

Everybody has different skill levels, but this was really hard and really, really, really fast.

Did I say it was really fast?

创建者 Krishna

May 22, 2019

Course content is very nice and covered aptly. I feel that some where more depth was necessary to understand the algorithms.

创建者 Bhagyashree B

May 09, 2020

Other than the subtle mistakes, the overall course was very informative. I wish there were more practise exercises though

创建者 Mohamed S

Mar 26, 2020

A comprehensive course by a wold class university,some teaching could have been better by using more interactive methods.

创建者 Ekun K

Jul 16, 2020

This is a great course. I recommend using the Introduction to Machine Learning book to complement the lecture videos.

创建者 Wynona R N

Jun 23, 2020

Good introduction course on machine learning algorithms. The books and the readings are recommended to look through!

创建者 Amanda V C

Jun 02, 2018

You will learn a lot. But the course is a little bit fast for regular students. Assignments deal with real problems.

创建者 Rohith S

Nov 17, 2017

A few more code examples would have helped better understand various packages provided by Python and how to use them

创建者 lcy9086

Feb 03, 2019

Great course on doing machine learning use sklearn and put little but enough explanation of the theories behind it!

创建者 Alexandr S

Feb 24, 2019

It would be nice to have more practical assignments like the last one! Anyway it was very interesting! Thank you!

创建者 Bharat G

Aug 30, 2017

Amazing Course but Please add some more theory and concepts in Neural Networking.Overall it is a good experience.

创建者 Alpan A

Nov 27, 2019

Very good curriculum with a hands on project. However thera are some limitations with the platform with grading

创建者 am

Jun 21, 2017

Complete course on supervised learning

Would be nice to cover PCA and unsupervised learning in the assignments

创建者 CMC

Feb 09, 2019

A little dated. Overall a good introduction. The informal explanation of SVM was particularly effective.