Machine Learning
ML/dictionary/ml
Definition
A subset of AI where the system learns patterns from data instead of following hand-written rules. The output is a model — a set of learned numbers that maps inputs to outputs. Spam filters, recommendation systems, and credit-risk scorers are classical ML.
Example
Gmail's spam filter learns which emails you mark as junk and updates its model — that's machine learning, not a rule someone wrote.
Related terms
Posts that use this term
- The runtimes: llama.cpp, Ollama, LM Studio
llama.cpp is the engine. Ollama and LM Studio wrap it. What each one does, when to reach for which, and why the OpenAI-compatible APIs are mostly but not entirely interchangeable.
- From models to LLMs
An LLM is one kind of ML model, trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.
- How a model learns: training and inference
Training is the expensive one-time event where a model's numbers get tuned. Inference is the cheap repeated use afterwards. The gap in cost is enormous, and it shapes the whole industry.
- What makes a model: data and algorithm
A model is a file of learned numbers, produced by running an algorithm over data. Both ingredients matter, but bad data beats a good algorithm every time.
- Inside AI: machine learning and deep learning
Open the AI umbrella. Machine learning is the part that learns from data. Deep learning is ML done with neural networks, and that's where today's models live.
- AI, in plain words
What "AI" actually means, where the term came from, and why every product calls itself AI now. Sets up where machine learning and deep learning fit underneath.
- Install Docker
Get Docker running on macOS, Linux, or Windows, confirm it with hello-world, and dodge the licensing and resource traps that trip people up.