Machine Learning
Thursday, October 21, 2021
Data Science Made Real
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Workshop Overview:
Join us to learn some best practices to apply data science technology to transform your business. Currently, 80% of advanced predictive analytics solutions using AI and Machine Learning technologies fail. Reason: - lack of knowledge and methodology to the diverse team of Business Analysts, Product Managers and Data Analysts. - Failure to collaboratively develop solutions that effectively address business needs. The knowledge you would gain will help you gain practical ways to address these challenges.
Intended Audience:
You are a product or project manager to apply data science/AI capabilities in your business solution You are an executive to leverage AI capabilities in transforming your business You are a business analyst to ensure right data science solution is developed
Topics Covered:
Analytics project lifecycle and how it differs from traditional project development Best practices to engage diverse team to develop clear business problem Ways to measure solution success
Workshop Takeaways:
Learn research questions to ask to apply data science in business Learn project management techniques to successfully deliver data science solutions
Pragmatic Machine Learning
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Atif Farid Mohammad PhD
Onriva/UNC Charlotte, Chief Science Officer/Artificial Intelligence Professor, AdjunctWorkshop Overview:
The use of Machine Learning in the arena of Social Determinants of Health.
Intended Audience:
- Data Scientists
- Machine Learning Engineers
Topics Covered:
- Python
- Data Acquisition
- Feature Extraction and Extrapolation
- Machine Learning Model Design
Workshop Takeaways:
- Comprehend the outcomes of Machine Learning Models.
To-Do Before Workshop / How to Prepare:
- Anaconda, Python installed on participant's laptops
Improving Cyber Threat Detection with Machine Learning, Visualizations and Graph Analytics
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Workshop Overview:
This workshop will take a look at how Graph Analytics can help improve the cybersecurity posture of any organization by implementing solutions like TigerGraph. Let us show you how to get more value out of the tools you have invested in without having so much to review.
Intended Audience:
This workshop is for SOC Staff, NOC Staff, Security Team Members, CISO, CTO, Technology Analysts.
Topics Covered:
* Database Landscape * Evolution of Graph * Cybersecurity Improvements * Machine Learning and AI with Graph * Threat Detection and Profiling
Workshop Takeaways:
How to improve your posture, gain more value from current investments and to get more from your current security stack with more accuracy, less alert fatigue and more automation.
Friday, October 22, 2021
Testing Your Models in Production With Drifter-ML
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Drifter-ML is a novel framework for testing machine learning models in depth. In this talk I will be walking participants through the central idea that powers drifter-ml, as well as showing how to use drifter-ml to test your models in production. An understanding of precision, recall and classifiers is expected, as well as understanding of the scikit-learn API.
Improving Cyber Threat Detection with Machine Learning, Visualizations and Graph Analytics
Join on CrowdcastWorkshop Overview:
This workshop will take a look at how Graph Analytics can help improve the cybersecurity posture of any organization by implementing solutions like TigerGraph. Let us show you how to get more value out of the tools you have invested in without having so much to review.
Intended Audience:
This workshop is for SOC Staff, NOC Staff, Security Team Members, CISO, CTO, Technology Analysts.
Topics Covered:
* Database Landscape * Evolution of Graph * Cybersecurity Improvements * Machine Learning and AI with Graph * Threat Detection and Profiling
Workshop Takeaways:
How to improve your posture, gain more value from current investments and to get more from your current security stack with more accuracy, less alert fatigue and more automation.
