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: Master AI with Minimal Data
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
September 2025
Taught in English
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There are 3 modules in this course
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.
What's included
3 videos3 readings1 assignment1 plugin
In this lesson, learners will examine how pretrained models, semantic embeddings, and transfer learning enable generalization in low-data environments. They'll break down each component’s role through hands-on exercises and visualizations—gaining clarity on how models can recognize patterns or make predictions with minimal labeled data.
What's included
4 videos2 readings1 assignment1 plugin
In this lesson, learners will evaluate and apply zero-shot and few-shot strategies—such as prompt engineering, meta-learning, and prototypical networks—to real-world tasks. Through scenario-based activities and model comparisons, learners will learn how to choose and implement the right method based on data limitations and task requirements.
What's included
4 videos1 reading3 assignments1 plugin
Instructor
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