用于机器学习的数据

Data for Machine Learning

525 次查看
阿尔伯塔机器科学研究院
Coursera
  • 完成时间大约为 13 个小时
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

课程概况

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:

Understand the critical elements of data in the learning, training and operation phases
Understand biases and sources of data
Implement techniques to improve the generality of your model
Explain the consequences of overfitting and identify mitigation measures
Implement appropriate test and validation measures.
Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering.
Explore the impact of the algorithm parameters on model strength

To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).

This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

课程大纲

What Does Good Data look like?

We all know that data is important for machine learning success, but what does it really look like? What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation.

Preparing your Data for Machine Learning Success

Now that you have your data sources identified, you need to bring it all together. This week describes what you need to prepare data overall.

Feature Engineering for MORE Fun & Profit

Data is particular to a problem. This week we'll discuss how to turn generic data into successful fuel for specific machine learning projects.

Bad Data

There are so many ways data can go wrong! This week discussed some of the pitfalls in data identification and processing.

千万首歌曲。全无广告干扰。
此外,您还能在所有设备上欣赏您的整个音乐资料库。免费畅听 3 个月,之后每月只需 ¥10.00。
Apple 广告
声明:MOOC中国十分重视知识产权问题,我们发布之课程均源自下列机构,版权均归其所有,本站仅作报道收录并尊重其著作权益。感谢他们对MOOC事业做出的贡献!
  • Coursera
  • edX
  • OpenLearning
  • FutureLearn
  • iversity
  • Udacity
  • NovoEd
  • Canvas
  • Open2Study
  • Google
  • ewant
  • FUN
  • IOC-Athlete-MOOC
  • World-Science-U
  • Codecademy
  • CourseSites
  • opencourseworld
  • ShareCourse
  • gacco
  • MiriadaX
  • JANUX
  • openhpi
  • Stanford-Open-Edx
  • 网易云课堂
  • 中国大学MOOC
  • 学堂在线
  • 顶你学堂
  • 华文慕课
  • 好大学在线CnMooc
  • (部分课程由Coursera、Udemy、Linkshare共同提供)

© 2008-2020 MOOC.CN 慕课改变你,你改变世界