Machine Learning & AI Courses & Certificates

Learn about the latest in artificial intelligence.

Artificial intelligence (AI) mimics the human capacity for problem-solving. Machine learning (ML) is the branch of AI that trains on and learns from existing data to deliver relevant results. UCLA Extension offers expert-led courses exploring both.

Enhance your understanding or advance your career with our offerings designed for enthusiasts and professionals alike. Whether you're looking for a deep-dive into the technical aspects of ML or to explore the ethical and policy implications of AI, we have the perfect courses for you.

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Machine Learning & AI

Courses

Problem Identification & AI Foundations for Safety-Critical Environments

This hands-on course prepares aerospace professionals to identify, design, prototype, and govern practical AI solutions for safety-critical environments. Students work with real operational problems and experiment with current AI tools and platforms to build AI-assisted workflows, evaluate deployment options, and develop a pilot-ready AI system concept.

The Data Behind Modern AI

Discover how strong data models, exploratory analysis, and modern datastores prepare high‑quality inputs for AI, with practical skills drawn from real analytics and AI projects.

The Human Side of AI: Designing, Working, and Living in an AI-Driven World

AI is reshaping how people think, work, and interact. This session shows how design choices influence human behavior and how AI can enhance, not replace, core human problem‑solving and creativity.

Trustworthy Machine Learning

Get equipped with theoretical and practical skills to build trustworthy machine learning systems, focusing on generative AI, model reliability, safety, privacy, fairness, and compliance through hands-on projects using industry-standard tools.

“Dangerous with Tools” — Secure AI Architecture & Local Deployment

This hands-on course teaches students how to responsibly stack AI tools and platforms within safety-critical constraints. Students experiment with secure AI architectures, compare cloud and local deployment options, apply data classification, and design governance safeguards for aerospace workflows.

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