你将学到什么
Discuss the use of lagged variables in time series forecasting
Explore the use of overlay data in time series forecasting
Identify several different applications of data mining with Weka
Compare incremental and non-incremental implementations of classifiers
Evaluate the performance of classifiers under conditions of concept drift
Classify tweets using various techniques
Calculate optimal parameter values for non-linear support vector machines
Demonstrate the use of R classifiers in Weka
Develop R commands and R scripts from Weka
Explain how distributed Weka runs Weka on a cluster of machines
Experiment with distributed implementations of Weka classifiers and clusterers
Explain how “map” and “reduce” tasks are used to distribute Weka
Design Python and Groovy scripts for Weka operations
Apply Python libraries to produce sophisticated visualizations of Weka output
Describe how Weka can be invoked from within a Python environment
课程概况
This course will bring you to the wizard level of skill in data mining, following on from Data Mining with Weka and More Data Mining with Weka, by showing how to use popular packages that extend Weka’s functionality. You’ll learn about forecasting time series and mining data streams. You’ll connect up the popular R statistical package and learn how to use its extensive visualisation and preprocessing functions from Weka. You’ll script Weka in Python – all from within the friendly Weka interface. And you’ll learn how to distribute data mining jobs over several computers using Apache SPARK.
课程大纲
Time series analysis
Data stream mining
Incremental classifiers
Evolving data streams
Support vector machines
Accessing data mining in R
Distributed data mining
Map-reduce framework
Scripting data mining in Python and Groovy
Applications: Soil analysis, Sentiment analysis, Bioinformatics, MRI neuroimaging, Image classification
面向人群
This course is aimed at anyone who deals in data. You should have completed Data Mining with Weka and More Data Mining with Weka – or be an experienced Weka user. Although the course includes some scripting with Python, you need no prior knowledge of the language. You will have to install and configure some software components; we provide full instructions.
