Machine Learning Fundamentals

# Machine Learning Fundamentals

Machine learning bridges statistics and computer science, enabling computers to learn patterns from data without explicit programming.

## Learning Objectives

In this lecture, you will learn:
- Supervised vs. unsupervised learning
- Classification and regression algorithms
- Model evaluation and validation
- Feature engineering and selection
- Practical implementation strategies

## Supervised Learning

**Classification**: Predicting categorical outcomes
- Logistic regression
- Decision trees
- Random forests
- Support vector machines
- Neural networks

**Regression**: Predicting continuous outcomes
- Linear regression
- Polynomial regression
- Ridge and Lasso regression
- Random forest regression

## Unsupervised Learning

**Clustering**: Finding hidden patterns
- K-means clustering
- Hierarchical clustering
- DBSCAN

**Dimensionality Reduction**:
- Principal Component Analysis (PCA)
- t-SNE
- UMAP

## Model Evaluation

- Cross-validation techniques
- Performance metrics
- Bias-variance tradeoff
- Overfitting prevention

## Practical Considerations

- Data preprocessing
- Feature engineering
- Hyperparameter tuning
- Model interpretation