Hands-on Lab: Machine Learning Using SQL and Apache Zeppelin Notebooks

IMPORTANT:
Registration through this link is required for this meetup.
https://eventreg.oracle.com/profile/web/index.cfm?PKwebID=0x704369abcd

Please bring your own laptop to this workshop.
Oracle will provide Free $500 Oracle Cloud credits to run this workshop. You are not required to put your credit card information for this trial.

6:00 PM - 6:20 PM Registration/Food & Beverages
6:20 PM - 6:30 PM Welcome Announcement & Short Keynote on ML
6:30 PM - 7:45 PM Hands-on Lab - Machine Learning using SQL and Apache Zeppelin Notebooks
7:45 PM - 8:00 PM Q&A and closing comment

KEYNOTE ABSTRACT:

The presentation will provide an overview of Machine Learning techniques and features in Oracle Machine Learning, with integrated notes in Autonomous Database, to easily train and deploy models from data in database tables.

WORKSHOP ABSTRACT:

In this lab, you will learn the fundamentals of Machine Learning using Apache Zeppelin Notebooks and SQL running on Autonomous Data Warehouse. These SQL notebooks, based on Apache Zeppelin technology, enable teams to collaborate to build, evaluate and deploy predictive models and analytical methodologies in the Oracle ADW. No prior ML Experience is required. We will start by covering the fundamentals of big data and machine learning using SQL, then build 2 practical ML Models for Fraud Detection and Sales Forecasting using Cloud Notebooks. Finally, you will use the ML Model to make Predictions and render them in an interactive Data Visualization.

WORKSHOP INSTRUCTOR:

Girish Adiga is a Consulting member technical staff at Oracle, working on Autonomous Health features in Oracle Autonomous Database product. Oracle Autonomous Health Framework uses applied machine learning techniques to proactively identify issues before they affect the health of database clusters and take actions to fix or alert the administrators. He has been working for the database product development team at Oracle for the past 18 years, currently focused on anomaly detection and predictive analytics to enable quick root cause analysis for database issues using machine learning.