Machine Learning Foundations for Developers
This workshop introduces developers to the fundamentals of machine learning, focusing on practical techniques for understanding, building, and integrating models into applications. Participants will learn the differences between supervised, unsupervised, and semi-supervised learning, along with the essential steps in a machine learning lifecycle including data generation and preprocessing, model building, evaluating, and implementation.
Using this foundational knowledge, we will move into understanding responsible AI (RAI) practices, covering topics like fairness (bias evaluation and mitigation), explainability, and privacy. Attendees will explore actionable strategies to incorporate RAI principles into their workflows, ensuring models are not only functional but also ethical.
Throughout this workshop, we will use hands-on exercises using Python-based tools and provided datasets, with options to work either on personal laptops or hosted environments like Google Colab.
Computer setup
Bring your own laptop with python 3.x installed.
Michelle Frost is an AI and machine learning practitioner, Microsoft AI MVP, and AI Advocate at JetBrains. She is also the founder of Accountable Intelligence, where she consults on AI governance and responsible AI strategy.
She holds a Master of Science in Artificial Intelligence from Johns Hopkins University and brings over a decade of experience across data science, engineering, and enterprise AI systems to questions of fairness, accountability, transparency, privacy, and risk. Her work spans AI and machine learning strategy, responsible AI development, and governance for high-risk domains, with a particular interest in building systems that keep humans meaningfully in the lead.
Michelle speaks on AI ethics, accessibility, inclusion, and the human impact of emerging technologies. Outside of work, Michelle can usually be found in her garden or with her nose in a book.