Tech Webinar: Big Data: Introduzione al Machine Learning ed all'Analisi Predittiva - Mario Cartia
1 like•852 views
This document introduces machine learning as a scientific discipline that focuses on algorithms capable of learning from data to make predictions. It outlines various use cases across industries including marketing, logistics, risk management, healthcare, life sciences, and travel. Additionally, it mentions Apache Mahout as a project aimed at providing scalable machine learning algorithms.
Tech Webinar: Big Data: Introduzione al Machine Learning ed all'Analisi Predittiva - Mario Cartia
- 1. BIG DATA: Introduzione al Machine Learning ed all’analisi predittiva Mario Cartia [email protected] Lunedì 26 Gennaio 2015 Ore 17:15
- 3. Machine Learning “Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data” “Such algorithms operate by building a model based on inputs and using that to make predictions or decisions” (source: Wikipedia)
- 4. Machine Learning: Use Cases • Marketing – Predicting Lifetime Value (LTV) – Customer segmentation – Cross selling/Recommendation – Product mix – Channel optimization – …
- 5. Machine Learning: Use Cases
- 6. Machine Learning: Use Cases • Logistics – Demand forecasting • Risk – Credit risk – Fraud detection • Healthcare – Medical resources allocation – Alerting and diagnostics from real-time patient data
- 7. Machine Learning: Use Cases • Life Sciences – Predicting prescription adherence with different approaches to reminding patients – Image analysis or GCMS analysis in a high throughput manner – Leveraging molecule database with metabolic stability data to elucidate new stable structures
- 8. Machine Learning: Use Cases • Travel – Aircraft scheduling – Dynamic pricing – Tourism forecasting • Other – Sentiment analysis – Loyalty programs – …
- 9. Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification
- 11. Prediction: The Easy Way