Supervised Learning

➺ Core Components:

  • Labeled datasets
  • Input-output pairs
  • Model training
  • Validation process 

    • ➺ Technical Implementation:

    Learning Types:
  • Classification
  • Binary classification
  • Multi-class classification
  • Multi-label classification
  • Regression
  • Linear regression
  • Polynomial regression
  • Time series prediction 

    • ➺Popular Algorithms:

  • Neural Networks
  • Feed-forward networks
  • CNN for image processing
  • RNN for sequences
  • Traditional Methods
  • Random Forests
  • Gradient Boosting
  • SVM variants 

    • ➺ Applications & Use Cases:

    Real-World Examples
  • Email spam detection
  • Credit risk assessment
  • Disease diagnosis
  • Price prediction
  • Face recognition
  • Sentiment analysis 

    • ➺ Industry Solutions:

  • Financial forecasting
  • Quality control
  • Customer churn prediction
  • Recommendation systems 

    • ➺ Best Practices:

  • Cross-validation
  • Feature engineering
  • Model selection
  • Hyperparameter tuning