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

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Course Overview

What You'll Learn

  • 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.

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.

Course FAQs

Is this an accredited online course?

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Can this course be used for continuing education credits?

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How do I enroll in this online school program?

To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'Zero-Shot & Few-Shot Learning: Master AI with Minimal Data' on the Coursera Instructor Network online class platform, where you can complete your registration.