Description:
In today’s fast-paced business environment there is a lot of buzz around how transformative Machine Learning (ML) can be. But how exactly does ML work? And does it have applications in demand forecasting?
Recent research suggests Machine Learning can improve your forecasting. But although ML offers a powerful set of tools to enhance forecasts, many people find it intimidating.
This free educational webinar will demystify the world of forecasting with ML, making it accessible and understandable for both novice and seasoned forecasting professionals.
In this one-hour webinar Sarah Darin will discuss:
• The fundamentals of Machine Learning in forecasting
• How to identify scenarios where ML can improve your forecasting accuracy
• How to use Forecast Pro’s completely automatic ML option
• Practical tips and strategies for building custom ML models in Forecast Pro
Join us to understand how Machine Learning can transform your forecasting process and empower your organization to make better data-driven decisions.
Can’t attend the live webinar? Register for the session and we will notify you via email when the recording is available for on-demand viewing.
Speaker:
Sarah Darin has 20 years of experience with statistical consulting, sales forecasting, regression modeling and marketing analytics. Sarah holds a Master’s of Science in Statistics from the University of Chicago, where she also served as a Lecturer for two years. She has consulted for clients across a broad range of industries, including Consumer Packaged Goods, Telecommunications, Technology, Retail, Automotive and Finance. Before joining BFS, Sarah was Vice President of Consulting Services at Nielsen where she focused on custom analytic solutions for the CPG and Expanded Vertical practices, teaching customers how to efficiently integrate, manage, model and forecast large-scale datasets. Sarah’s ability to understand and explain statistical concepts in the context of real-world, messy data makes her an ideal instructor for this workshop. Sarah received her undergraduate degree in Applied Mathematics from Harvard University.
