This content is designed for audience without any prior Machine learning knowledge. It starts from very basics and goes to advanced topics. We will try to keep this content live and include more and more advanced lab sessions with real life scenarious. Thanks for your support and feedback to make this content better.
Using an EPOC headset from Emotiv, I have captured 14 channels of EEG (brain waves) while subjects lied and answered truthfully to a series of questions. I fed this labelled dataset into Azure Machine Learning to build a classifier which predicts whether a subject is telling the truth or lying. In this session, I will share my results on this "lie detector" experiment. I will show my machine learning model, data cleaning process, and results, along with discussing the limitations of my approach and next steps/resources. Attendees will gain exposure to the Emotiv EPOC headset and Azure Machine Learning.
I have demonstrated how you can set up the MLOps quickstart code from a GitHub Repository in Azure DevOps. I am going to switch gears to provisioning Azure Machine Learning on Azure, a tool to "empowering developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models. Create an Azure Machine Learning workspace to train, manage, and deploy machine-learning experiments and web services." Let's supply the following details: Give Azure some time for deploying resources via Azure Resource Manager (ARM). Afterwards, please launch and "try the new Azure Machine Learning Studio".