Your First AI Project: Create Intelligent Applications from Scratch

Natasha Brown is a machine learning engineer and educator with over a decade of experience building AI systems in production environments. She began her career as a software engineer at Microsoft, where she worked on search relevance and recommendation systems, before moving into dedicated ML roles at startups and large technology companies. She has led the development of AI platforms used by millions of people, including real-time fraud detection systems, natural language interfaces for enterprise software, and computer vision pipelines for autonomous inspection.

Natasha holds a Master of Science in Computer Science from Stanford University, where her research focused on transfer learning and efficient neural network architectures. She has published work at NeurIPS and ICML on topics including model compression, few-shot learning, and evaluation methodologies for production AI systems. She is a frequent speaker at industry conferences and has delivered workshops and talks at PyCon, O'Reilly AI Conference, and the ML Conference series.

She is the founder of AI Foundations, an organization that provides practical, project-based AI education for software engineers and data professionals transitioning into machine learning. Through her writing, teaching, and open-source contributions, she advocates for accessible AI education, reproducible research practices, and responsible deployment of machine learning systems. She lives in Seattle with her family and maintains an active GitHub presence where she publishes example projects, tutorials, and tooling for the ML community.