Machine Learning Foundations

Core Concepts

Data and Features

• Data Types: Numerical, Categorical, Ordinal
• Feature Engineering: Creating meaningful features from raw data
• Data Quality: Handling missing values, outliers, and noise
• Data Preprocessing: Scaling, normalization, encoding

Model Training

• Training Process: How models learn from data
• Loss Functions: Measuring model performance
• Optimization: Finding the best model parameters
• Validation: Ensuring model generalization

Model Evaluation

• Performance Metrics: Accuracy, precision, recall, F1-score
• Cross-validation: Robust model evaluation
• Overfitting and Underfitting: Common challenges
• Model Selection: Choosing the right algorithm

Statistical Foundations

Probability Theory

• Random Variables: Discrete and continuous
• Probability Distributions: Normal, Binomial, Poisson
• Joint and Conditional Probability: Dependencies between variables
• Bayes' Theorem: Fundamental for many ML algorithms

Statistical Learning

• Parameter Estimation: Maximum likelihood, MAP
• Hypothesis Testing: Statical significance
• Confidence Intervals: Uncertainty quantification
• Bias-Variance Tradeoff: Key concept in model performance

Mathematical Foundations

Linear Algebra

• Vectors and Matrices: Basic operations
• Matrix Decomposition: SVD, eigendecomposition
• Vector Spaces: Linear independence, basis
• Linear Transformations: Important for neural networks

Calculus

• Derivatives: Optimization fundamentals
• Gradients: Multi-variable calculus
• Chain Rule: Essential for backpropagation
• Optimization Methods: Gradient descent variants

Programming Foundations

Python for ML

• NumPy: Numerical computing
• Pandas: Data manipulation
• Scikit-learn: ML algorithms
• TensorFlow/PyTorch: Deep learning frameworks

Best Practices

• Code Organization: Project structure
• Version Control: Git basics
• Documentation: Code and model documentation
• Testing: Unit tests and validation