What is Artificial Intelligence?
Artificial Intelligence (AI) is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. It encompasses reasoning, learning, perception, and language understanding.
AI can be categorized into:
- Narrow AI: Designed for a specific task (e.g., voice assistants, recommendation engines).
- General AI: Possesses human‑level reasoning across many domains (still theoretical).
Key techniques include expert systems, fuzzy logic, and neural networks.
Machine Learning Basics
Machine Learning (ML) is a subset of AI that focuses on the ability of machines to learn from data without being explicitly programmed.
ML algorithms can be divided into three main types:
- Supervised Learning: Models learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Models find patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Agents learn by interacting with an environment and receiving rewards.
Popular libraries: scikit‑learn, TensorFlow, PyTorch.
Key Terminology
- Algorithm: A set of rules a model follows to solve a problem.
- Model: The output of training that can make predictions.
- Dataset: Collection of data used for training and testing.
- Feature: An individual measurable property or characteristic of a phenomenon.
- Label: The output variable in supervised learning.
- Training vs. Inference: Training builds the model; inference uses it for predictions.
Getting Started
Ready to dive deeper? Check out these tutorials: