Zero-Shot & Few-Shot Learning: Master AI with Minimal Data

Coursera MOOC / Non-credit USD 49

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About this course

Zero-Shot & Few-Shot Learning is an intermediate-level course designed for data scientists, ML engineers, and AI practitioners who want to build models that perform well—even when labeled data is limited. Traditional supervised learning breaks down when examples are scarce or tasks are constantly evolving. This course shows you how to solve that problem using cutting-edge zero-shot and few-shot learning techniques. You'll learn how to apply pre trained models, semantic embeddings, and transfer learning to generalize across tasks without retraining from scratch. Through case-driven videos, hands-on labs, and decision-focused projects, you'll explore tools like prompt engineering, prototypical networks, and contrastive learning. Along the way, you'll build and defend full pipelines tailored to real-world constraints—choosing the right method based on data availability, task requirements, and deployment goals. Whether you're diagnosing fraud with few samples or classifying new product types without labels, this course will equip you to build smarter, leaner models that learn more with less.

What you'll learn

Apply zero-shot and few-shot learning techniques to solve problems with limited labeled data
Use pre-trained models and transfer learning to generalize across tasks
Implement prompt engineering strategies for language and vision models
Build prototypical networks and apply contrastive learning methods
Design complete ML pipelines that balance data availability, task requirements, and deployment constraints
Select appropriate few-shot methods based on real-world problem characteristics

Course objectives

Master zero-shot and few-shot learning approaches for data-scarce environments
Build models that perform well on evolving tasks without constant retraining
Develop practical skills in prompt engineering and semantic embeddings
Create and defend end-to-end machine learning pipelines for minimal-data scenarios