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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
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该课程共有3个模块
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.
涵盖的内容
3个视频3篇阅读材料1个作业1个插件
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.
涵盖的内容
4个视频2篇阅读材料1个作业1个插件
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.
涵盖的内容
4个视频1篇阅读材料3个作业1个插件
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