Making Machine Learning Accessible

Learn the business “why” and technical “how” for implementing machine learning (ML) in your organization.

This webinar covers:

  • Overview of ML and its importance to industries
  • Common use cases (fraud detection, image detection, etc.)
  • Resources needed (data, skills, etc.)
  • Technical steps to implementing ML, from data preprocessing to training and deploying ML models

 

Presenters:

Pete Garcin, Developer Advocate, ActiveState

Pete is the Developer Advocate at ActiveState with over 15 years of software development experience in games & open source. He is passionate about engaging with communities & dedicated to enhancing developers’ experiences.

Nathan Greeneltch, Technical Consulting Engineer, Intel
Nathan’s role is to help drive customer engagements for Python as well as Intel’s libraries. Previously he spent 3 years in the processor development side of Intel as a ML practitioner in the defects division. Nathan has a PhD in physical chemistry from Northwestern University, where he worked on nanoscale lithography of metal wave-guides for amplification of laser-initiated vibrational signal in small molecules.

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