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Webinar - Machine Learning & AI Foundations: A Guide to Predictive Modeling
This Webinar is over| Date | Apr 30, 2019 |
| Time | 12:00 PM EDT |
| Cost | Free |
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Online
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Join NetCom Learning trainer along with Managed Learning Services leader NetCom Learning for a complimentary interactive webinar on "Machine Learning & AI Foundations: A Guide to Predictive Modeling".
Organizations are faced with increasing volumes of data, but how much of that data exactly do we need to use in our predictive analytics models, and at what point should we be worried that we might have to upgrade our modeling infrastructure?
In this session, we will walk through the modeling process from start to finish and discuss how the amount of data fluctuates, often dramatically, at different stages of the project. We will review each stage—data selection, data preparation, modeling, scoring, and deployment—with scalability in mind, providing IT professionals, data scientists, and leadership with new insights, perspectives, and collaboration tools.
Date: 04/30/2019
Time: 1 P.M. Eastern Standard Time/ 10 A.M. Pacific Standard Time
REGISTER HERE: https://goo.gl/niYYfX
Agenda:
Organizations are faced with increasing volumes of data, but how much of that data exactly do we need to use in our predictive analytics models, and at what point should we be worried that we might have to upgrade our modeling infrastructure?
In this session, we will walk through the modeling process from start to finish and discuss how the amount of data fluctuates, often dramatically, at different stages of the project. We will review each stage—data selection, data preparation, modeling, scoring, and deployment—with scalability in mind, providing IT professionals, data scientists, and leadership with new insights, perspectives, and collaboration tools.
Date: 04/30/2019
Time: 1 P.M. Eastern Standard Time/ 10 A.M. Pacific Standard Time
REGISTER HERE: https://goo.gl/niYYfX
Agenda:
- Evaluating the proper amount of data
- Assessing data quality and quantity
- Data preparation and data modeling challenges
- Scoring machine-learning models
- Deploying models and adjusting data prep and scoring
- Monitoring and maintenance
- Q&A with the speaker
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