Introduction to Machine Learning for Time-series Forecasting
Introduction to Machine Learning for Time-series Forecasting


This workshop will provide an overview of how to use machine learning to forecast complex operational problems. This workshop is a hands-on, Python-based, introduction to how machine learning can be used to tackle time-series problems. Topics to be covered include: why time-aware ML problems are different from non-time-aware ML problems; why time-series and forecasting problems in particular are challenging; and how to use to leverage both deep-learning and non-deep-learning approaches to successfully tackle real world problems. This course is a code-based workshop using a variety of Python based tools so familiarity with Python and data science fundamentals will be helpful, but time series experience is not assumed.


As Data Science Engineering Architect at DataRobot, Mark designs and builds key components of automated machine learning infrastructure. He contributes both by leading large cross-functional project teams and tackling challenging data science problems. Before working at DataRobot and data science he was a physicist where he did data analysis and detector work for the Olympus experiment at MIT and DESY.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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