Chevron Left
返回到 Applied Machine Learning in Python

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

4.6
星
8,251 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

OA

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

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：

1326 - Applied Machine Learning in Python 的 1350 个评论（共 1,499 个）

创建者 Adesh T

•

Feb 8, 2022

It was amazing journey to complete this course.

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.

创建者 Tianyu Z

•

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 CHAPPIDI S N B

•

Jan 16, 2022

Excellent way of teaching and learned well

创建者 Souvik M

•

Jan 23, 2022

where is my certificate?????????????????

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.

创建者 ROHIT J

•

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

创建者 xingkong

•

Aug 9, 2017

quiz is harder than assignment.

创建者 shreyash t

•

Jul 28, 2020

overalll good way to start ml

创建者 Vaibhav S

•

May 27, 2020

way better than last teacher.

创建者 Nicolas B

•

Jul 5, 2017

Muy buen curso, muy completo.

创建者 Sanskriti G

•

Oct 26, 2021

it was an amazing experience

创建者 李祥泰

•

Aug 15, 2017

Nice courses with nice quiz!

Coursera Footer

开拓职业生涯或促进职业发展

热门课程和证书

热门收藏和文章

在线获得学位或证书

Coursera

社区

更多

随时随地学习
Placeholder
Placeholder
Placeholder