AI & ML
AI & Machine Learning
Artificial Intelligence (AI) enables software and machines to perform tasks associated with human intelligence. Machine Learning (ML) lets systems learn patterns from data and use them to make predictions or recommendations.
Tools and technologies
- Python
- PyTorch
- TensorFlow
- scikit-learn
- Jupyter
- Hugging Face
- OpenAI API and other model APIs
Practical examples
- A property-search assistant that understands “three-bedroom house near Freetown”.
- A crop-disease classifier that analyses a photo of a leaf.
- A customer-support assistant that answers questions from approved company documents.
Application areas
- Predictive maintenance
- Document and image understanding
- Recommendation systems
- Speech and language tools
- Forecasting and decision support
Suggested hands-on project
Mini project: create a small, clearly labelled dataset of property listings, train a basic model to estimate likely price ranges, compare predictions with actual prices, and explain uncertainty. Do not present model estimates as professional valuations.
A typical workflow
Define a useful problem → collect lawful, representative data → prepare and split the data → train or configure a model → evaluate errors and bias → deploy with human oversight → monitor results.
How to get started
Choose one small, measurable problem, build a prototype with sample data, document assumptions, test common failure cases, and collect feedback before expanding it.
Responsible use: AI can produce incorrect or biased outputs. Protect personal data, test with local examples, disclose limitations, and keep a human responsible for important decisions.