Start with machine learning fundamentals

AI project work requires more than calling an API. Begin with Python, data handling, supervised learning, validation and evaluation. Learn how data quality and problem framing affect every result.

Select a manageable problem

Choose a project with a measurable objective and an accessible dataset. Examples include text classification, document search, image classification or a recommendation prototype. Define what success means before selecting a model.

Build a complete project

  1. Document the user problem and constraints.
  2. Explore and prepare the data.
  3. Create a simple baseline.
  4. Train and compare appropriate approaches.
  5. Evaluate errors, bias, privacy and limitations.
  6. Present the result through a small application or API.

Use generative AI responsibly

If a project uses a large language model, document prompts, retrieval sources, evaluation cases and failure modes. Do not upload private information to tools without authorisation, and do not present generated output as guaranteed fact.

What a mentor should review

Useful feedback covers problem definition, code quality, evaluation and communication—not only whether a demo appears impressive. Your final presentation should clearly separate measured results from assumptions.