Chevron Left
返回到 Applied Machine Learning in Python

学生对 University of Michigan 提供的 Applied Machine Learning in Python 的评价和反馈

4.6
星
8,771 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

PS

Apr 2, 2018

Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!

JL

Aug 19, 2018

Concise and clear presentation of the material with the majority of time focused around using TDD to learn and practice concepts through developing solutions to open ended coding challenges.

筛选依据：

1401 - Applied Machine Learning in Python 的 1425 个评论（共 1,599 个）

创建者 KOSHAL K

•

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 Wissal A

•

Feb 19, 2023

Tests the aspects treated in the learning phase

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

•

Feb 28, 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 Ujjwal O

•

Jul 5, 2023

more visual explanations would be better!!!!

创建者 Amita A D

•

May 17, 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 Athatharv D T 2

•

Dec 22, 2025

it was a great course to learn about ML

创建者 Alperen B O

•

Dec 15, 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 18, 2019

Gives good overview of ML using Pyton

创建者 Mehrdad A

•

Sep 22, 2024

It was summerized and beneficial

创建者 Shreekant G

•

Jul 17, 2019

Really taught best ML algorithms