Design Flawless A/B Tests: Uncover Insights

Overview

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.

Syllabus

Module 1: Evaluate Experiment Bias Sources

Learners will systematically identify and assess bias sources that compromise A/B test validity, focusing on novelty effects and exposure inequality detection.

Module 2: Design Statistically Valid Experiments

Learners will apply power analysis principles to calculate appropriate sample sizes and design experiments that reliably detect meaningful business impacts.