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學生對 IBM 提供的 使用 Python 进行机器学习 的評價和反饋

4.7
9,725 個評分
1,581 條評論

課程概述

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....

熱門審閱

RC

Feb 07, 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.

RN

May 26, 2020

Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!

篩選依據:

1301 - 使用 Python 进行机器学习 的 1325 個評論(共 1,566 個)

創建者 Dorjee G

Nov 12, 2019

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

Thanks,

創建者 Mahendra S

Jul 21, 2019

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

創建者 Mujeebullah Y

Oct 23, 2019

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

創建者 Hemanth A

Jul 06, 2020

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

創建者 Dhruv V C

May 10, 2020

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

創建者 Ayushman S

Apr 06, 2020

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

創建者 Abhinav K

Mar 03, 2019

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

創建者 KOSHAL K

Feb 11, 2020

It is best for beginners for introduction to machine learning.

創建者 Prakash R

Feb 10, 2019

This course helps me to get understand about Machine Learning.

創建者 Ashis G

Jun 27, 2020

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

創建者 Erfan H

Apr 08, 2020

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

創建者 Sathishkumar

Mar 14, 2020

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

創建者 Lakshmi m s

May 16, 2020

this is best course for learning machine learning in python

創建者 Ana C

Jul 31, 2019

I missed algorithms like random forest and ensemble Methods

創建者 Shreenivas R D

Jul 02, 2020

Best course for beginners or to get better knowledge in ML

創建者 Tobias B

May 12, 2020

Course gives a good overview over differente ML techniques

創建者 Prince R

Apr 17, 2020

Covered important topics and hands on was pretty good too.

創建者 yavuz k

Jul 16, 2019

Very good structured course. Everything stepwise explained

創建者 伊藤 成美

May 01, 2020

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

創建者 Qidi L

Mar 13, 2019

Great online course for Machine Learning! very practical!

創建者 Brendan W

Apr 08, 2020

Excellent course, lots of typos in the lab instructions.

創建者 Thomas

Jun 26, 2020

This is a good course I wished it was more challenging.

創建者 Abdul M A

May 14, 2019

very good lecture but not detailing notes to back it up

創建者 Vishal b p

Jul 27, 2020

this course was amazing for statistical field student

創建者 Jayant D

Jul 29, 2019

Explanation is good but code practice could be better.