The Guide to AI Machine Learning

Machine Learning (ML) is a branch of artificial intelligence (AI) that focuses on developing systems capable of learning and improving from experience without being explicitly programmed. Instead of following predefined rules, ML models use algorithms to analyse data, identify patterns, and make predictions or decisions.

Key Concepts of Machine Learning

1. Data

• ML relies on large datasets as the foundation for training models.
• Data can be structured (e.g., spreadsheets) or unstructured (e.g., images, text).

2. Algorithms

• Algorithms process data and adjust model parameters to optimise predictions.
• Examples: Linear regression, decision trees, neural networks.

3. Learning types

• Supervised Learning: Models learn from labeled data to make predictions. Example: Predicting house prices based on features like size and location.
• Unsupervised Learning: Models uncover patterns in unlabeled data. Example: Grouping customers into segments using clustering.
• Reinforcement Learning: Models learn through trial and error by receiving rewards or penalties. Example: Training robots to walk.

4. Applications

• Healthcare: Disease prediction and diagnosis.
• Finance: Fraud detection and risk assessment.
• Retail: Personalised recommendations.
• Autonomous Systems: Self-driving cars and drones.

Machine learning powers many modern technologies, enabling systems to adapt and improve in real time, making it foundational to the advancement of AI.

‍

‍