Core Concepts in Machine Learning

This section covers the fundamental concepts that form the foundation of machine learning.

Data Fundamentals

Types of Data

  • Structured Data: Tables, databases
  • Unstructured Data: Text, images, audio
  • Semi-structured Data: JSON, XML
  • Time Series Data: Sequential observations

Data Quality

  • Completeness: Missing values
  • Accuracy: Correctness of values
  • Consistency: Data integrity
  • Timeliness: Data freshness

Data Preprocessing

  • Cleaning: Handling missing values, outliers
  • Transformation: Scaling, normalization
  • Feature Engineering: Creating new features
  • Data Integration: Combining data sources

Learning Paradigms

Model Types

  • Discriminative Models: Learn decision boundaries
  • Generative Models: Learn data distribution
  • Parametric Models: Fixed parameters
  • Non-parametric Models: Flexible parameters

Learning Approaches

  • Batch Learning: Learn from all data at once
  • Online Learning: Learn incrementally
  • Instance-based Learning: Memory-based learning
  • Model-based Learning: Parameter-based learning

Model Complexity

  • Capacity: Model's ability to learn
  • Bias-Variance Tradeoff: Model flexibility
  • Regularization: Controlling complexity
  • Model Selection: Choosing right complexity

Training Process

Optimization

  • Loss Functions: Measuring error
  • Gradient Descent: Parameter updates
  • Learning Rate: Step size
  • Convergence: Finding optimal solution

Validation

  • Train-Test Split: Data division
  • Cross-validation: Model validation
  • Hyperparameter Tuning: Model configuration
  • Model Selection: Choosing best model

Evaluation

  • Metrics: Performance measures
  • Error Analysis: Understanding mistakes
  • Model Comparison: Benchmarking
  • Statistical Tests: Significance testing

Model Deployment

Production Considerations

  • Scalability: Handling large data
  • Latency: Response time
  • Resource Usage: Computational efficiency
  • Monitoring: Performance tracking

Maintenance

  • Model Updates: Retraining
  • Version Control: Model versioning
  • Documentation: Code and model docs
  • Testing: Quality assurance

Integration

  • API Development: Service endpoints
  • Pipeline Building: Workflow automation
  • Monitoring: System health
  • Error Handling: Failure management