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 does, when to pick 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
Install Docker Desktop on macOS and Windows, Docker Engine on Linux. Verify with docker run hello-world and learn the licensing and resource gotchas.