Machine Learning (ML): A subset of artificial intelligence that enables computers to learn from data patterns and make decisions or predictions without explicit programming.
Algorithm: A step-by-step procedure or set of rules used by ML models to analyze data and identify patterns.
Model: The mathematical or computational representation trained by an algorithm on data, used to make predictions or classifications.
Features: Input variables or attributes used by the model to make predictions (e.g., age, income).
Labels: The output or target variable that the model aims to predict or classify (e.g., spam or not spam).
Training Data: A dataset used to teach the model by adjusting its parameters based on input-output pairs.
Machine learning systems learn from data rather than relying on explicit instructions for each task.
It encompasses various types, including supervised, unsupervised, and reinforcement learning, each suited for different problems.
1. What is machine learning primarily defined as?
2. What is the primary purpose of an algorithm in machine learning?
3. Who developed the Perceptron, an early neural network model, in 1957?
Machine Learning — definition?
Computers learn from data to make decisions.
Machine Learning — definition?
Subset of AI enabling data-driven decisions.
Milestone — Perceptron?
An early neural network model for binary classification.
Algorithm — role?
Analyzes data and finds patterns.
Supervised Learning — role?
Uses labeled data to train predictive models.
Model — what?
Representation trained to make predictions.
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