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Introduction to Machine Learning

Machine learning is a subfield of artificial intelligence
that focuses on building systems that learn from data. Un-
like traditional programming, where explicit instructions
are provided, machine learning algorithms identify pat-
terns and make decisions with minimal human interven-
tion.

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The three main types of machine learning are supervised
learning, unsupervised learning, and reinforcement learn-
ing. Each type addresses different kinds of problems and
uses different approaches to learn from data.

Supervised learning uses labeled training data to predict
outcomes for new, unseen data. Common applications in-
clude classification (spam detection, image recognition)
and regression (price prediction, risk assessment).

Machine Learning Fundamentals
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Unsupervised learning finds hidden patterns in unlabeled
data. Clustering and dimensionality reduction are common
techniques used in customer segmentation, anomaly de-
tection, and feature extraction.

Introduction to Machine Learning

Machine learning is a subfield of artificial intelligence that focuses on building systems that learn from data. Unlike traditional programming, where explicit instructions are provided, machine learning algorithms identify patterns and make decisions with minimal human intervention.

The three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Each type addresses different kinds of problems and uses different approaches to learn from data.

Supervised learning uses labeled training data to predict outcomes for new, unseen data. Common applications include classification (spam detection, image recognition) and regression (price prediction, risk assessment).

Unsupervised learning finds hidden patterns in unlabeled data. Clustering and dimensionality reduction are common techniques used in customer segmentation, anomaly detection, and feature extraction.