Data Mining - Classification

Questo Corso è parte del

Pathway in Introduction to Data Mining


Learn how to formulate and solve classification problems for use in Data Mining and Business Intelligence applications such as; fraud detection, customer churning, network intrusion detection, etc... You will learn how to develop, validate and apply a data mining workflow to solve binary and non-binary classification problems. The course is self-contained, and it does not require any programming skills. Hands-on lectures are based on the KNIME open source software platform.

Frequenza e Attestati

Frequenza
GRATUITO!
Attestato di Partecipazione
GRATUITO!

Categoria

Informatica, Gestione e Analisi dei Dati

Ore di Formazione

45

Livello

Base

Modalità Corso

Tutoraggio

Lingua

English

Durata

4 Settimane

Tipologia

Online

Stato del Corso

Tutoraggio Soft

Avvio Iscrizioni

4 Apr 2016

Apertura Corso

22 Apr 2016

Inizio Tutoraggio

2 May 2016

Fine Tutoraggio

30 Jun 2016

Tutoraggio Soft da

1 Jul 2016

Chiusura Corso

Non impostato
By the end of this course, you will be able to: 

  • develop a Data Mining workflow for solving a classification problem, 
  • apply elementary missing replacement strategies, 
  • apply pre-processing techniques including dimensionality reduction, 
  • select and deploy the “optimal classifier” (whatever it means) also taking into account decision costs, 
  • select relevant attributes and remove not relevant and/or redundant attributes. 

You will learn all this using the KNIME open source platform, which integrates power and expressiveness of Weka, R and Java.

Basic knowledge of probability, statistics and mathematics.

  • Pang-Ning Tan, Steinbach Michael and Vipin Kumar, (2006). Introduction to Data Mining. Morgan-Kaufmann.
The course spans four weeks. Each week requires 8 to 10 hours of work. Each week consists of 5 to 7 video-lectures. Each video-lecture consists of a methodology video, a software usage video and a practice session.

You must accomplish all practice sessions associated with lectures, and then upload, to the course platform, the corresponding KNIME workflow you developed .


FABIO STELLA

FABIO STELLA

Department of Informatics, Systems and Communication