Machine learning 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 breakdown of core concepts:
1. Types of Machine Learning
- 📚 Supervised Learning
Uses labeled data to train models (e.g., regression, classification). - 🧭 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.
2. Key Components
- 📊 Features: Input variables used to predict outcomes.
- 🎯 Labels: Target variables the model aims to predict.
- 🧠 Model: The mathematical representation of patterns in data.
3. Applications
- 📈 Predict stock prices
- 📝 Spam detection
- 🧪 Medical diagnosis
- 📸 Image recognition
For deeper exploration, check our Advanced ML Concepts guide.