Machine Learning
A subset of AI where systems automatically learn and improve from experience without being explicitly programmed.
In-depth explanation
Machine learning algorithms build mathematical models based on training data to make predictions or decisions. The three main types are supervised learning (learning from labeled examples), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error). ML powers many modern applications from email filters to medical diagnosis.
Examples
More in AI Fundamentals
Artificial Intelligence (AI)
The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.
Deep Learning
A subset of machine learning using artificial neural networks with multiple layers to model complex patterns.
Algorithm
A set of step-by-step instructions or rules followed to solve a problem or perform a computation.
Model
A mathematical representation learned from data that can make predictions or decisions on new, unseen data.
Training
The process of teaching a machine learning model to make predictions by exposing it to data and adjusting its parameters.
Inference
The process of using a trained model to make predictions on new, unseen data.
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