Advanced Data Mining with Weka
Course Features
Duration
5 weeks
Delivery Method
Online
Available on
Lifetime Access
Accessibility
Mobile, Desktop
Language
English
Subtitles
English
Level
Intermediate
Effort
4 hours per week
Teaching Type
Self Paced
Course Description
Course Overview
Alumni Network
International Faculty
Post Course Interactions
Instructor-Moderated Discussions
Skills You Will Gain
What You Will Learn
Apply Python libraries to produce sophisticated visualizations of Weka output
Calculate optimal parameter values for non-linear support vector machines
Classify tweets using various techniques
Compare incremental and non-incremental implementations of classifiers
Demonstrate the use of R classifiers in Weka
Describe how Weka can be invoked from within a Python environment
Design Python and Groovy scripts for Weka operations
Develop R commands and R scripts from Weka
Discuss the use of lagged variables in time series forecasting
Evaluate the performance of classifiers under conditions of concept drift
Experiment with distributed implementations of Weka classifiers and clusterers
Explain how distributed Weka runs Weka on a cluster of machines
Explain how map and reduce tasks are used to distribute Weka
Explore the use of overlay data in time series forecasting
Identify several different applications of data mining with Weka
Target Students
Although the course includes some scripting with Python, you need no prior knowledge of the language
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
You will have to install and configure some software components; we provide full instructions
Course Instructors
Ian Witten
Instructor