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
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.