Introduzione al Corso
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.Informatica, Gestione e Analisi dei Dati
Ore di Formazione45
LivelloBase
Modalità CorsoTutoraggio
English
Durata4 Settimane
TipologiaOnline
Stato del CorsoTutoraggio Soft
Agenda del Corso
Avvio Iscrizioni
Apertura Corso
Inizio Tutoraggio
Fine Tutoraggio
Tutoraggio Soft
Chiusura Corso
Risultati Attesi
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.
Pre-requisiti
Basic knowledge of probability, statistics and mathematics.
Libri di testo e letture consigliate
- Pang-Ning Tan, Steinbach Michael and Vipin Kumar, (2006). Introduction to Data Mining. Morgan-Kaufmann.
Formato del corso
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.Regole per ottenere gli Attestati e sostenere gli Esami
Attestato di Partecipazione
You must accomplish all practice sessions associated with lectures, and then upload, to the course platform, the corresponding KNIME workflow you developed .
