eval-docs/machine-learning-primer.md

Machine Learning: A Beginner's Guide

What is Machine Learning?

Machine learning is a subset of artificial intelligence where systems learn patterns from data rather than being explicitly programmed. Instead of writing rules, you provide examples and let the algorithm discover the rules.

Types of Machine Learning

Supervised Learning

The algorithm learns from labeled examples.

Classification: Predicting categories

• Email spam detection
• Image recognition
• Medical diagnosis

Regression: Predicting continuous values

• House price prediction
• Stock price forecasting
• Temperature prediction

Common algorithms:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines (SVM)
• Neural Networks

Unsupervised Learning

The algorithm finds patterns in unlabeled data.

Clustering: Grouping similar items

• Customer segmentation
• Document categorization
• Anomaly detection

Dimensionality Reduction: Simplifying data

• Feature extraction
• Visualization
• Noise reduction

Common algorithms:

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• t-SNE

Reinforcement Learning

The algorithm learns through trial and error, receiving rewards or penalties.

Applications:

• Game playing (AlphaGo, chess)
• Robotics
• Autonomous vehicles
• Resource management

The Machine Learning Pipeline

1. Data Collection: Gather relevant data
2. Data Cleaning: Handle missing values, outliers
3. Feature Engineering: Create useful features
4. Model Selection: Choose appropriate algorithm
5. Training: Fit model to training data
6. Evaluation: Test on held-out data
7. Deployment: Put model into production
8. Monitoring: Track performance over time

Key Concepts

Overfitting vs Underfitting

Overfitting: Model memorizes training data, performs poorly on new data

• Solution: More data, regularization, simpler model

Underfitting: Model too simple to capture patterns

• Solution: More features, complex model, less regularization

Train/Test Split

Never evaluate on training data. Common splits:

• 80% training, 20% testing
• 70% training, 15% validation, 15% testing

Cross-Validation

K-fold cross-validation provides more robust evaluation:

1. Split data into K folds
2. Train on K-1 folds, test on remaining fold
3. Repeat K times
4. Average the results

Bias-Variance Tradeoff

• High Bias: Oversimplified model (underfitting)
• High Variance: Overcomplicated model (overfitting)
• Goal: Find the sweet spot

Evaluation Metrics

Classification

• Accuracy: Correct predictions / Total predictions
• Precision: True positives / Predicted positives
• Recall: True positives / Actual positives
• F1 Score: Harmonic mean of precision and recall
• AUC-ROC: Area under receiver operating curve

Regression

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R-squared (R2)

Getting Started

1. Learn Python and libraries (NumPy, Pandas, Scikit-learn)
2. Work through classic datasets (Iris, MNIST, Titanic)
3. Take online courses (Coursera, fast.ai)
4. Practice on Kaggle competitions
5. Build projects with real-world data

Remember: Machine learning is 80% data preparation and 20% modeling. Start with clean data and simple models before going complex.