Machine Learning

Machine learning (ML) is a branch of artificial intelligence where systems learn from data rather than following explicitly programmed rules.

Traditional programming:

Rules + Data → Output

You write rules; the computer follows them.

Machine learning:

Data + Outputs → Rules (Model)

You provide examples; the computer learns patterns.

Example: Instead of writing rules for "what makes an email spam," you show thousands of spam and non-spam emails. The ML system learns to distinguish them.

Machine learning is powerful when patterns are complex, rules would be brittle, or you have lots of labeled examples.

Supervised learning: Learn from labeled examples. Most common type.

• Classification: Is this email spam? (Yes/No)
• Regression: What price will this house sell for? ($X)
• Examples: Spam filters, price prediction, medical diagnosis

Unsupervised learning: Find patterns in unlabeled data.

• Clustering: Group similar customers together
• Dimensionality reduction: Compress data while preserving information
• Examples: Customer segmentation, anomaly detection

Reinforcement learning: Learn by trial and error with rewards/penalties.

• Agent takes actions in environment
• Receives rewards or penalties
• Learns to maximize reward
• Examples: Game playing, robotics, recommendation systems

Self-supervised learning: Create labels from data itself. Used in modern LLMs.

• Predict next word in text
• Predict masked words
• Examples: GPT, BERT training

1. Problem definition: What are you trying to predict or classify? What data do you have?

2. Data collection: Gather relevant data. More quality data generally = better models.

3. Data preparation: Clean data, handle missing values, format appropriately.

4. Feature engineering: Select and transform relevant variables. (Less critical in deep learning.)

5. Model selection: Choose algorithm appropriate to problem and data.

6. Training: Feed data through model, adjust parameters to minimize error.

7. Evaluation: Test on held-out data. Measure accuracy, precision, recall, etc.

8. Iteration: Adjust approach based on results. Repeat until satisfied.

9. Deployment: Put model into production. Monitor performance.

10. Maintenance: Retrain as data distribution changes. Models degrade over time.

For classification:

• Logistic regression: Simple, interpretable baseline
• Random forests: Ensemble of decision trees
• Support vector machines: Find optimal decision boundaries
• Neural networks: Learn complex patterns

For regression:

• Linear regression: Simple relationships
• Gradient boosting: Powerful ensemble method
• Neural networks: Complex relationships

For clustering:

• K-means: Group into k clusters
• DBSCAN: Density-based clustering
• Hierarchical: Tree of clusters

For dimensionality reduction:

• PCA: Linear projection
• t-SNE: Visualization
• Autoencoders: Neural network compression

Modern LLMs: Technically supervised learning (predict next token) but scale and architecture create emergent capabilities beyond traditional ML.

Overfitting: Model memorizes training data, fails on new data. Solution: More data, regularization, simpler model, cross-validation.

Underfitting: Model too simple to capture patterns. Solution: More complex model, better features, more training.

Data quality: Garbage in, garbage out. Solution: Data cleaning, validation, quality processes.

Bias: Training data biases affect predictions. Solution: Diverse training data, bias audits, fairness constraints.

Interpretability: Complex models are hard to explain. Solution: Simpler models, SHAP values, attention visualization.

Drift: Data distribution changes over time. Solution: Monitoring, regular retraining, drift detection.

Cold start: No data for new users/items. Solution: Default models, active learning, similarity-based recommendations.

For most use cases: Don't train models—use pre-trained ones via APIs:

• OpenAI, Anthropic for text
• Cloud vision APIs for images
• No-code platforms for specific tasks

To learn ML:

1. Start with Python and pandas for data manipulation
2. Learn scikit-learn for traditional ML
3. Explore deep learning with PyTorch or TensorFlow
4. Practice on Kaggle datasets

When to build custom ML:

• Unique problem without existing solutions
• Proprietary data that provides competitive advantage
• Performance requirements existing solutions can't meet
• Cost optimization at scale

Resources:

• Fast.ai: Practical deep learning course
• Andrew Ng's courses: ML fundamentals
• Kaggle: Practice competitions
• Hugging Face: Pre-trained models and tutorials