Machine Learning (ML) is a branch of AI where computers learn from data instead of being explicitly programmed with rules. Rather than telling a computer exactly what to do in every situation, you feed it examples and let it figure out the patterns on its own.
How It Works
At its core, machine learning involves:
- Training data — a large dataset the model learns from
- A model — a mathematical structure that finds patterns in the data
- Training — the process of adjusting the model until it gets good at predictions
- Inference — using the trained model to make predictions on new data
Types of Machine Learning
- Supervised learning — trained on labeled data (e.g., this is a cat, this is a dog)
- Unsupervised learning — finds patterns in unlabeled data on its own
- Reinforcement learning — learns by trial and error, receiving rewards for correct actions
Real-World Examples
- Spam filters learning to identify junk email
- Netflix recommending shows you might like
- Credit card fraud detection
- Image recognition in your phone’s photo app
- ChatGPT and other language models
Machine learning is the engine powering most of today’s AI tools — including the ones transforming how websites are built and managed.
Related: Deep Learning
Related: Artificial Intelligence
