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Zero-shot learning is when a model performs a task it was not explicitly trained on by leveraging general knowledge learned during pretraining.
It reduces the need for task-specific labelled data and speeds up experimentation on new use cases.
A model classifies customer feedback into new categories using only natural-language instructions and no labeled examples.
Transfer Learning
Transfer learning is a technique where a model trained on one task is reused as the starting point for a different but related task. Instead of training from scratch, you leverage the knowledge the model has already gained, which saves time, data, and computational resources.
Foundation Model
A foundation model is a large AI model trained on broad, diverse data that can be adapted for a wide range of downstream tasks. These models serve as a starting point — or foundation — that can be fine-tuned or prompted for specific applications.
Prompt Engineering
Prompt engineering is the practice of crafting and refining the instructions (prompts) given to an AI model to get the best possible output. It involves techniques like providing context, examples, and constraints to guide the model's response.
Our programme follows a structured Level 4 curriculum with project-based learning, practical workflows, and guided implementation across business and career use cases. Funded route available for UK citizens and ILR holders.