# Machine Learning Basics
Machine Learning (ML) is a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. It has revolutionized many industries and continues to advance rapidly.
## Types of Machine Learning
### Supervised Learning
Training a model on labeled data where the correct output is known. Examples include:
- Classification (spam detection, image recognition)
- Regression (price prediction, trend analysis)
### Unsupervised Learning
Finding patterns in unlabeled data. Examples include:
- Clustering (customer segmentation)
- Dimensionality reduction (data compression)
### Reinforcement Learning
Learning through trial and error with rewards and penalties. Examples include:
- Game playing (AlphaGo, Chess engines)
- Robotics control
- Autonomous vehicles
## Common Algorithms
1. **Linear Regression**: Predicting continuous values
2. **Logistic Regression**: Binary classification
3. **Decision Trees**: Hierarchical decision making
4. **Random Forests**: Ensemble of decision trees
5. **Neural Networks**: Deep learning models
6. **K-Means**: Clustering algorithm
7. **Support Vector Machines**: Classification with margins
## Applications
- Healthcare: Disease diagnosis, drug discovery
- Finance: Fraud detection, algorithmic trading
- E-commerce: Recommendation systems
- Transportation: Self-driving cars
- Natural Language: Translation, chatbots
The field of machine learning is constantly evolving with new techniques and applications emerging regularly.
