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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

8,053 个评分


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.


Sep 8, 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


626 - Applied Machine Learning in Python 的 650 个评论(共 1,465 个)

创建者 AliYavari

Jun 20, 2022

It was a great start through my journey toward data science.

创建者 Veronika S

Oct 10, 2021

It is a great course! :) Clearly explained and good practice


Oct 11, 2020

Very complete course, great professor and a good assignments

创建者 Mega D

Oct 15, 2019

Great class with very informative examples, and applicatoins


Sep 18, 2019

Course was fully explained in details and with good exercise

创建者 Ayon B

Oct 19, 2018

Good course. And challenging indeed, especially the quizzes.

创建者 Sathvik K

Aug 28, 2018

great for learning how to practically apply machine learning

创建者 sunil s

Jul 5, 2018

Great course for implementing machine learning using python.

创建者 Wai Y P S

Jun 22, 2021

Thanks you so much University of Michigan for Great course

创建者 Fernando G

Mar 16, 2021

Excellent course! Well paced and you end up learning a lot!

创建者 Hafiz A Q

May 18, 2020

A very nice course from the implementation point of view!!!

创建者 Mikhailov R

Jan 27, 2019

Sometimes the lecturer is boring but overall perfect course

创建者 Maciej W

Jul 8, 2018

Very informative, broad, hands-on course. Strong recommend.

创建者 nitin p

Feb 28, 2018

Very Interesting and fascinating Course of Machine Learning

创建者 Biju S

Oct 12, 2017

Very tough to finish. Big gap with material and assignments

创建者 Amoghavarsha B

Mar 19, 2020

Perfect course with lots of assignments and good material!

创建者 Yu S

Jul 16, 2018

Good applied material to study along theoretical material!

创建者 Marek S

Sep 29, 2017

Useful, practical use of sklearn to machine learning tasks

创建者 Matias B M

Aug 14, 2017

Challenging and rewarding. Wouldn't have it any other way.

创建者 Dipanjan S

Jun 24, 2017

Excellent clarity, recommended for getting started with ML

创建者 Mohd M K

Oct 29, 2021

Learned a lot!! Thanks coursera for this wonderful course

创建者 sung w c

Sep 25, 2017

Very well organized and useful for hands-on application.

创建者 Hiroki U

Nov 29, 2020

Last assignment was very good for understanding ML task.

创建者 Joga j

Jan 24, 2022

very good course and many practice labs good

创建者 Mehrar I

Oct 10, 2020

this course is really helpful to learn machine learning