Uploaded August 2026 | Updated September 2026, 3 weeks ago
microchip.com/masters
What does it take to build a successful machine learning application?
In this instructor spotlight from MASTERs 2026, George discusses the importance of data collection, sensor selection, and data preparation before machine learning inferencing even begins. He shares what students learn in his class and why understanding the fundamentals of data acquisition is essential to building effective AI and machine learning solutions.
During the course, attendees work with a PIC33A microcontroller and a spectrometer sensor to collect light data, perform dimensionality reduction, and explore Python-based preprocessing techniques. The hands-on approach helps students understand how data quality directly impacts machine learning outcomes.
In this interview, you'll learn about:
-The value of hands-on technical training at MASTERs
-Key steps before machine learning inferencing
-Data collection and sensor selection best practices
-Using PIC33A devices and spectrometer sensors for data acquisition
-Why "garbage in, garbage out" remains one of the most important concepts in AI and machine learning
MASTERs gives engineers the opportunity to learn directly from Microchip experts, gain practical experience with emerging technologies, and apply those concepts to real-world designs.
microchip.com/masters
What does it take to build a successful machine learning application?
In this instructor spotlight from MASTERs 2026, George discusses the importance of data collection, sensor selection, and data preparation before machine learning inferencing even begins. He shares what students learn in his class and why understanding the fundamentals of data acquisition is essential to building effective AI and machine learning solutions.
During the course, attendees work with a PIC33A microcontroller and a spectrometer sensor to collect light data, perform dimensionality reduction, and explore Python-based preprocessing techniques. The hands-on approach helps students understand how data quality directly impacts machine learning outcomes.
In this interview, you'll learn about:
-The value of hands-on technical training at MASTERs
-Key steps before machine learning inferencing
-Data collection and sensor selection best practices
-Using PIC33A devices and spectrometer sensors for data acquisition
-Why "garbage in, garbage out" remains one of the most important concepts in AI and machine learning
MASTERs gives engineers the opportunity to learn directly from Microchip experts, gain practical experience with emerging technologies, and apply those concepts to real-world designs.
