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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
8,061 个评分

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

AS

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.

FL

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!!

筛选依据:

676 - Applied Machine Learning in Python 的 700 个评论(共 1,467 个)

创建者 Haozhe ( X

May 31, 2020

Great course. Love the design for each assignments.

创建者 Archunan G

Dec 3, 2019

Course is interesting and nice . quiz made well .

创建者 MAINAK C

Aug 20, 2019

very nice and apt course for all types of learners.

创建者 Lutz H

Jun 17, 2019

Really well explained. Great excersices! Well done!

创建者 AMAN K

Mar 6, 2019

Course Material is quite interesting and practical.

创建者 Mai N

Sep 8, 2018

Good starting points for any machine learning folks

创建者 Ebenezer A W

Nov 14, 2017

A really nice course to begin machine learning with

创建者 LEE D D

Nov 9, 2017

Perfect and hard course than Andrew Ng's ML course!

创建者 A A

Aug 4, 2017

Best introduction to sklearn library I came across!

创建者 zhang y

Oct 3, 2021

comprehensive machine learning course for beginner

创建者 Pratama A A

Jul 14, 2020

If you're beginner i suggest dont take this course

创建者 Ameya B

Jul 3, 2020

Overall good intro to actually using scikit-learn.

创建者 likejian

May 14, 2020

It’s very nice course to learn ML for the new guys

创建者 Abdelrahman M s A

Feb 26, 2018

One of the best practical ML courses in the field!

创建者 Arun S

Nov 9, 2017

Great professor with lot of real world experience.

创建者 ChanLung

Jul 31, 2017

Excellent Machine Learning Course for application!

创建者 Rui J (

Nov 16, 2021

it is so much fun to write programms on your own!

创建者 Oxana M

Dec 7, 2020

I like this cource. It gives a very good overview

创建者 Bauyrzhan A

Nov 15, 2020

It is decent course with fair level of complexity

创建者 Anuj P

Jun 20, 2020

tremendous knowledge for applied machine learning

创建者 SUBBA R D

Jun 11, 2020

Very useful course especially for the beginners .

创建者 Raúl V - S

Apr 17, 2020

Very well organized and challenging real datasets

创建者 Prachi A

Mar 1, 2020

Amazing course for a beginner in Machine Learning

创建者 Juan S

Feb 2, 2020

Good overview to a lot of different ML techniques

创建者 Dongquan S

Oct 9, 2019

Very well designed course. Learned a lot. Thanks!