We may earn an affiliate commission when you visit our partners.

Zero-Shot & Few-Shot Learning

Master AI with Minimal Data

Hurix Digital

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.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Two deals to help you save

Save money when you learn. Here are two deals and offers that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until December 2

Annual Subscription

Invest in your career growth and save. Start your annual subscription to Coursera Plus and access 10,000+ programs.

Valid until March 12, 2027

Save 40% Off 3 Months

Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.

What's inside

Syllabus

Lesson 1: Foundations First: Zero-Shot & Few-Shot Learning Demystified

In this introductory lesson, learners will explore the core principles of zero-shot and few-shot learning, including how they In this introductory lesson, learners will explore the core principles of zero-shot and few-shot learning, including how they differ In this introductory lesson, learners will explore the core principles of zero-shot and few-shot learning, including how they differ from traditional supervised learning. Through clear examples and intuitive analogies, learners will build a foundational understanding of these approaches and why they matter in modern machine learning.understanding of these approaches and why they matter in modern machine learning.understanding of these approaches and why they matter in modern machine learning.

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Career center

Learners who complete Zero-Shot & Few-Shot Learning: Master AI with Minimal Data will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Zero-Shot & Few-Shot Learning: Master AI with Minimal Data.

Artificial Intelligence

Save

The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.

Artificial Intelligence: A Modern Approach

Hardcover

$$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

Save

The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.

Zero-Shot & Few-Shot Learning

Two deals to help you save

What's inside

Syllabus

Save this course

Career center

Reading list

Share

Similar courses

Coursera Buyer Protection & Guarantee

7-Day Free Trial & Free Audit

14-Day Refund Guarantee

Verified Academic & Industry Partner