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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

12,859 个评分


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....



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.


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.


1826 - 使用 Python 进行机器学习 的 1850 个评论(共 2,240 个)

创建者 Jince P G

Nov 30, 2019

Great course focusing on the fundamental concepts of machine learning.


Jul 10, 2020

Very properly Explained, will love to learn more from the instructor.

创建者 Bimal T

Jun 3, 2020

The Course Content is good, but the code is not explained in details.

创建者 Thanh K ( V

Apr 26, 2020

most of the codes are too hard to follow, need more detail narrations

创建者 Dorjee G

Nov 11, 2019

Great course, great instructor. I enjoyed doing the Lab works.


创建者 Mahendra S

Jul 21, 2019

Contents are very useful and informative. A good start for beginners.

创建者 Mujeebullah Y

Oct 23, 2019

Good course. However, they need to explain the code more in details.

创建者 Alexandre N

Dec 21, 2020

Recommendation systems could receive a peer-reviewed task as well.

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


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


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