Expert Network - Machine Learning Tech Days
•Download as PPTX, PDF•
1 like•309 views
Presentation at Expert Network Tech Days about Machine Learning and practical applications with Azure ML Studio. For the second edition of Expert Network TechDays, we propose to discover together how Artificial Intelligence and Machine Learning are changing society and the foundations of how we build software solutions. High level presentations, real life examples and hands-on advice from our top developers will be included. During the two days, our guest speakers Stefano Tempesta and Vlad Iliescu, together with talented people of Expert Network will share with us the secrets of how the world is shaped by two of the current hotest topics in IT. http://www.expertnetwork.ro/techdays/
More Related Content
Similar to Expert Network - Machine Learning Tech Days
Similar to Expert Network - Machine Learning Tech Days (20)
More from Stefano Tempesta
More from Stefano Tempesta (20)
Recently uploaded
Recently uploaded (20)
Expert Network - Machine Learning Tech Days
- 1. Machine Learning Tech Day Stefano Tempesta Vlad Iliescu
- 2. AGENDA Introduction to Machine Learning Understanding Machine Learning Algorithms Machine Learning with Azure ML Studio Machine Learning with Python Hands-on Lab
- 3. Vlad Iliescu Stefano Tempesta STRONGBYTES
- 4. Introduction to Machine Learning Stefano
- 7. Machine Learning • Sentiment Analysis • Demand Estimation • Recommendations • Outcome Prediction • Anomaly Detection MODEL REST Service DATASET EXPERIMENT 1. (Feature Engineering) 2. Training 3. Scoring 4. Evaluation
- 9. Feature Engineering • Aggregated variables: aggregated transaction amount per account, aggregated transaction count per account in last 24 hours and last 30 days. • Mismatch values: mismatch between shipping Country and billing Country. • Risk tables: fraud risks are calculated using historical probability grouped by country, IP address, etc.
- 10. Training • Data cleansing • Missing data • Duplicate rows • Data formats • Scales • Choose the right trainer • Anomaly Detection • Classification • Clustering • Regression
- 11. Scoring • Create predictions by using new data based on patterns in the model • Use “score” (and not “prediction”) • A list of recommended items and a similarity score • Numeric values, for time series models and regression models • A probability value, indicating the likelihood that a new input belongs to some existing category • The name of a category or cluster to which a new item is most similar • A predicted class or outcome, for classification models
- 12. Evaluation • Evaluate the model to determine if the predictions are accurate
- 17. Clustering
- 18. Classification
- 19. Considerations for Algorithm Selection
- 20. Understanding Machine Learning Algorithms Vlad
- 21. Machine Learning with Azure ML Studio Stefano
- 23. Historical Dataset Feature Engineering Split Train / Score Check model quality Training algorithm Outcome prediction
- 25. Cache Hit Ratio Optimization
- 26. Classification
- 27. Machine Learning with Python Vlad
- 28. Hands-on Lab
- 29. CLASSIFICATION
- 31. REGRESSION 15°
- 32. CLUSTERING
- 33. REINFORCEMENT
Editor's Notes
- To enhance the predictive power of the ML algorithms, an important step is feature engineering, where additional features are created from raw data based on domain knowledge. For example, if an account has not made a big purchase in the last month, then a thousand-dollar transaction all of a sudden could be suspicious. The new features generated in this scenario include: Aggregated variables, such as aggregated transaction amount per account, aggregated transaction count per account in last 24 hours and last 30 days; Mismatch variables: such as mismatch between shippingCountry and billingCountry, which potentially indicates abnormal behavior. Risk tables: fraud risks are calculated using historical probability grouped by country, state, IPAddress, etc