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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

7,522 个评分
1,372 条评论


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....


Nov 26, 2020

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

Oct 13, 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!!


1201 - Applied Machine Learning in Python 的 1225 个评论(共 1,355 个)

创建者 Antti H

Oct 23, 2020

Good course, but the labs have quite a few bugs in them.

创建者 Wang Y

Feb 16, 2018

Good, despite some confusions in the lecture and quiz.

创建者 Tangudu S S

May 23, 2020

Got a very clear picture of ML usage in Data Science.

创建者 Yash B

May 7, 2020

It was little bit difficult specially the assignments

创建者 Abhishek R

May 27, 2018

Needed a better retrospect on final/week 4 assignment

创建者 Alexander C

Mar 11, 2018

Good introductory course. A lot of material covered.

创建者 Dr. F T

Aug 17, 2018

Good but I was expecting much details in some area.


Mar 1, 2020

Its a very good course for an intermediate level.

创建者 Vinay P d L R

Sep 26, 2017

goes too fast and too shallow to deserve 5 stars

创建者 Prerna A

Apr 27, 2021

The course is planned in a very structural way.

创建者 Anendra G

Apr 30, 2018

Awesome theory about machine learning concepts.

创建者 Catherine M

Mar 1, 2021

Nice course. A lot of ML models get presented.

创建者 harsh a

Feb 3, 2018

Good course.

Thanks to entire team

Harsh Arora.


Jun 19, 2019

Some concepts should be introduced in detail.

创建者 Amita D

May 18, 2018

Need more information about more algorithms

创建者 Ruben W

Sep 8, 2019

Best course so far in this specialisation

创建者 Alan F

Feb 28, 2018

Good course but there's a lot of material

创建者 Abdulwaheed M

Jun 17, 2020

Teaching is very good and it is helpfull

创建者 Alperen B O

Dec 16, 2020

I get late feedback for lab assignments

创建者 Ramya K

Jul 15, 2019

Well-organized but assignments too easy

创建者 Supratim D

Aug 10, 2017

Very informative but bit too difficult.


Aug 2, 2020

very helpfull.thanks for creating this

创建者 Xiang C

May 12, 2020

It's good to learn how to use sklearn.

创建者 Jagadish C A

Sep 19, 2019

Gives good overview of ML using Pyton

创建者 Shreekant G

Jul 17, 2019

Really taught best ML algorithms