This guide provides information and resources about some basic approaches in machine learning.
Machine learning is a branch of artificial intelligence where computers learn from data instead of being explicitly programmed. It involves algorithms that analyze and recognize patterns in data, allowing them to make predictions or decisions. Machine learning encompasses various types, including supervised learning with labeled data, unsupervised learning for pattern discovery, and reinforcement learning for sequential decision-making. Deep learning, a subset, employs deep neural networks for tasks like image recognition and natural language processing. Machine learning applications span diverse fields such as healthcare, finance, and self-driving cars, driving innovation and automation across industries.
Diagram source: linkedin.com/pulse/business-intelligence-its-relationship-big-data-geekstyle
Diagram source: https://machine-learning.paperspace.com/wiki/supervised-unsupervised-and-reinforcement-learning
Machine Learning can be divided into three approaches:
Supervised Learning – The model learns from labeled data, where input-output pairs are provided. Examples include classification (e.g., spam detection) and regression (e.g., predicting house prices).
Unsupervised Learning – The model learns patterns and structures from unlabeled data. Examples include clustering (e.g., customer segmentation) and dimensionality reduction (e.g., PCA).
Reinforcement Learning – The model learns by interacting with an environment and receiving rewards or penalties. Examples include robotics, game playing (e.g., AlphaGo), and self-driving cars.
