Design Flawless A/B Tests: Uncover Insights
By External Provider
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 will learn
- Experimental validity relies on controlling bias like novelty and unequal exposure to ensure reliable and trustworthy results.
- Power analysis sets proper sample sizes early, avoiding wasted effort on studies too weak to detect real effects.
- Strong experiment design balances statistical rigor with business limits to deliver feasible and sound testing.
- High-quality experiments shape better decisions, making bias control and sample sizing core data skills.