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

Abstract: 

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.

Bio: 

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
info@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from Youtube
Vimeo
Consent to display content from Vimeo
Google Maps
Consent to display content from Google