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返回到 使用 Python 进行机器学习

学生对 IBM 技能网络 提供的 使用 Python 进行机器学习 的评价和反馈

4.7
12,831 个评分

课程概述

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course....

热门审阅

RC

Feb 6, 2019

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

FO

Oct 8, 2020

I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.

筛选依据:

1826 - 使用 Python 进行机器学习 的 1850 个评论(共 2,230 个)

创建者 Hemanth A

Jul 6, 2020

A good platform for users curious about the various ML techniques.

创建者 Dhruv C

May 10, 2020

Marks were deducted for no reason in the peer graded assignment .

创建者 Alexios M

Mar 13, 2021

Well-structured course. It drives you stop-by-step in most cases.

创建者 Nitai S

Dec 8, 2021

n​otebook setups for final projects could have been much better.

创建者 mohd z

Jun 22, 2021

Awesome course with excellent content and Project based learning

创建者 Ayushman S

Apr 6, 2020

Some concepts were hurried. But jypyter notebooks are very good.

创建者 Abhinav K

Mar 2, 2019

A complete package for those who want to start from the stratch

创建者 AMLAN G

Jul 21, 2022

This is a very gd course for a student to learn ML with python

创建者 Subash L

Jun 26, 2021

Over all good. The lab results could be explained a bit better

创建者 KOSHAL K

Feb 11, 2020

It is best for beginners for introduction to machine learning.

创建者 Prakash R

Feb 10, 2019

This course helps me to get understand about Machine Learning.

创建者 Germán G R

Apr 27, 2022

It is a good course, they could put more practical exercises.

创建者 Ashis G

Jun 27, 2020

A little more hands on training on the videos were necessary.

创建者 Erfan H

Apr 8, 2020

it was a good course for learning the usages of python in ML

创建者 Sathishkumar

Mar 14, 2020

It is good one,I learned basic concepts of Ml in this course

创建者 Lakshmi m s

May 16, 2020

this is best course for learning machine learning in python

创建者 Ana C

Jul 31, 2019

I missed algorithms like random forest and ensemble Methods

创建者 Shruti j

Oct 6, 2020

must take thiis course if you want to learn ML thoroughly.

创建者 Shreenivas R D

Jul 2, 2020

Best course for beginners or to get better knowledge in ML

创建者 Tobias B

May 12, 2020

Course gives a good overview over differente ML techniques

创建者 Prince R

Apr 17, 2020

Covered important topics and hands on was pretty good too.

创建者 yavuz k

Jul 16, 2019

Very good structured course. Everything stepwise explained

创建者 yogita

Mar 5, 2022

the ml labs should be video based rather than documented.

创建者 srijani c

Oct 2, 2021

challenging course good introduction to machine learning.

创建者 成美伊藤

May 1, 2020

This course is one of the most worthiest contents for me.