Course Features
Duration
5 weeks
Delivery Method
Online
Available on
Lifetime Access
Accessibility
Mobile, Desktop
Language
English
Subtitles
English
Level
Beginner
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
Compare the performance of different mining methods on a wide range of datasets
Demonstrate how to set up learning tasks as a knowledge flow
Solve data mining problems on huge datasets
Apply equal-width and equal-frequency binning for discretizing numeric attributes
Identify the advantages of supervised vs unsupervised discretization
Evaluate different trade-offs between error rates in 2-class classification
Classify documents using various techniques
Debate the correspondence between decision trees and decision rules
Explain how association rules can be generated and used
Discuss techniques for representing, generating, and evaluating clusters
Perform attribute selection by wrapping a classifier inside a cross-validation loop
Describe different techniques for searching through subsets of attributes
Develop effective sets of attributes for text classification problems
Explain cost-sensitive evaluation, cost-sensitive classification, and cost-sensitive learning
Design and evaluate multi-layer neural networks
Assess the volume of training data needed for mining tasks
Calculate optimal parameter values for a given learning system
Target Students
This course is aimed at anyone who deals in data professionally or is interested in furthering their professional or academic skills in data science
Course Instructors
Course Reviews
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