Machine Learning (ML) is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. Here's a quick guide to its core concepts:
📌 Key Concepts
Supervised Learning 📊
Uses labeled data to train models (e.g., classification, regression).Unsupervised Learning 🧩
Finds hidden patterns in unlabeled data (e.g., clustering, dimensionality reduction).Reinforcement Learning 🏁
Learns by interacting with an environment through trial and error.
🧠 Core Algorithms
| Algorithm | Description |
|---|---|
| Linear Regression | Predicts continuous values using a linear relationship. |
| Decision Trees | Splits data into branches based on feature values. |
| K-Means Clustering | Groups data into clusters of similar instances. |
| Support Vector Machines (SVM) | Classifies data by finding optimal separating hyperplanes. |
🌍 Applications
- Healthcare 🩺: Predicting disease outbreaks.
- Finance 💰: Fraud detection.
- Recommendation Systems 🎯: Personalized content suggestions.
- Natural Language Processing (NLP) 📘: Sentiment analysis and chatbots.
📚 Further Reading
For deeper exploration, check our introduction to AI or advanced ML topics.