Machine Learning (ML)

Machine Learning (ML) is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed. ML algorithms build models based on sample data (training data) to make predictions or decisions.

Types of Machine Learning

Type	Description	Example
Supervised	Learning with labeled data	Spam detection
Unsupervised	Finding patterns in unlabeled data	Customer segmentation
Reinforcement	Learning through trial and error	Game playing AI
Semi-supervised	Mix of labeled and unlabeled data	Image classification

ML Algorithms

• Linear Regression: Predicting continuous values
• Decision Trees: Classification and regression
• Neural Networks: Deep learning models
• Random Forest: Ensemble method
• Support Vector Machines: Classification
• K-Means Clustering: Grouping data

ML Workflow

1. Data collection and preparation
2. Feature engineering
3. Model selection
4. Training the model
5. Evaluation and validation
6. Deployment
7. Monitoring and retraining

Popular ML Frameworks

• TensorFlow: Google's open-source framework
• PyTorch: Facebook's research-focused framework
• Scikit-learn: Python library for traditional ML
• Keras: High-level neural network API