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This workshop introduces the concept of Few-Shot Learning in machine learning, a technique designed for learning effective models from a very limited amount of data. The session begins with an overview of Few-Shot Learning, explaining how it differs from traditional machine learning approaches that typically require large datasets. We'll delve into the strategies and algorithms that make Few-Shot Learning possible, such as transfer learning, meta-learning, and data augmentation techniques. The workshop will also highlight real-world applications where Few-Shot Learning is particularly beneficial, like medical image diagnosis and natural language processing tasks with limited data. Throughout the session, participants will be guided through theoretical aspects and case studies to understand the practical implementation of these concepts. Note: Basic knowledge of machine learning concepts and some programming experience (ideally in Python) is recommended for this workshop.