Machine Learning (ML)
A subfield of AI focused on creating algorithms that learn from data.
Overview
Machine Learning (ML) is a subfield of Artificial Intelligence that develops algorithms and statistical models enabling computer systems to learn from data. Rather than relying on explicit, rule-based programming, these systems detect patterns and relationships in datasets, then use them to generate predictions or inform decisions.
What is Machine Learning?
Machine Learning builds on techniques from statistics, mathematics, and computer science to improve performance on a given task as it processes more data. By discovering how input features correlate to desired outputs, ML models can generalize these patterns to new scenarios.
How Does Machine Learning Work?
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Data Collection & Preparation
Gathering relevant data and transforming it into a suitable format for training. -
Model Selection & Training
Choosing an algorithm (e.g., neural networks, decision trees, or support vector machines) and providing it with training data to learn patterns. -
Prediction or Decision
Once trained, the model applies its learned patterns to new, unseen data, generating outputs such as classifications, recommendations, or forecasts. -
Evaluation & Iteration
Performance metrics (e.g., accuracy, precision, recall) guide model refinement to address potential issues like overfitting or underfitting.
Common Applications
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Medical Diagnosis and Healthcare
Analyzing patient data to assist with diagnostics or treatment recommendations. -
Image and Speech Recognition
Identifying objects or interpreting spoken language in various contexts. -
Natural Language Processing
Understanding, generating, or classifying text for tasks like machine translation or sentiment analysis. -
Financial Forecasting & Risk Assessment
Estimating market trends and spotting fraud. -
Autonomous Vehicles & Robotics
Supporting navigation, perception, and action planning.