Overview
Machine learning paradigms are essential frameworks that guide how machines learn from data. The main paradigms include supervised, unsupervised, semi-supervised, and reinforcement learning, each with unique characteristics and applications. Supervised learning uses labeled data to train models, whi...
Key Terms
Example: Predicting house prices based on historical data.
Example: Grouping customers based on purchasing behavior.
Example: Classifying images with a few labeled examples.
Example: Training a robot to navigate a maze.
Example: Decision trees are a type of algorithm used in supervised learning.
Example: A linear regression model predicting sales.
Related Topics
Deep Learning
A subset of machine learning that uses neural networks with many layers to analyze various factors of data.
advancedNatural Language Processing
A field of AI that focuses on the interaction between computers and humans through natural language.
intermediateData Mining
The process of discovering patterns and knowledge from large amounts of data.
intermediateComputer Vision
A field that enables computers to interpret and make decisions based on visual data.
advancedKey Concepts
Supervised LearningUnsupervised LearningSemi-Supervised LearningReinforcement Learning