利用Weka进行数据挖掘

Data Mining with Weka

Discover practical data mining and learn to mine your own data using the popular Weka workbench.

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新西兰怀卡托大学
FutureLearn
  • 完成时间大约为 5
  • 初级
  • 英语
注:本课程由FutureLearn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

你将学到什么

Demonstrate use of Weka for key data mining tasks

Evaluate the performance of a classifier on new, unseen, instances

Explain how data miners can unwittingly overestimate the performance of their system

Identify learning methods that are based on different flavors of simplicity

Apply many different learning methods to a dataset of your choice

Interpret the output produced by classification methods

Describe the principles behind many modern machine learning methods

Compare the decision boundaries produced by different classification algorithms

Debate ethical issues raised by mining personal data

课程概况

Today’s world generates more data than ever before! Being able to turn it into useful information is a key skill. This course introduces you to practical data mining using the Weka workbench. We’ll dispel the mystery that surrounds the subject. We’ll explain the principles of popular algorithms. We’ll show you how to use them in practical applications. You’ll get plenty of experience actually mining data during the course, and afterwards you’ll be well equipped to mine your own. Weka originated at the University of Waikato in NZ, and Ian Witten has authored a leading book on data mining.

课程大纲

What is data mining?

Where can it be applied?

How do simple classification algorithms work?

What are their strengths and weaknesses?

In what ways are real-life classification methods more complex?

How should you evaluate a classifier’s performance?

What is “overfitting” and how can you combat it?

How can ensemble techniques combine the result of different algorithms?

What ethical considerations arise when mining data?

面向人群

This course is aimed at anyone who deals in data. It involves no computer programming, although you need some experience with using computers for everyday tasks. High school maths should be more than enough and you’ll need an understanding of some elementary statistics concepts (means and variances).

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