Machine Learning Documentation

A practical guide to understanding and implementing machine learning systems.

ML has become the backbone of modern software — from recommendation engines and fraud detection to medical imaging and autonomous vehicles. This documentation covers the fundamentals, walks through practical code, and provides guidance for building production-ready ML systems.

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Quick Start

Get a model trained and predicting in under 10 lines of Python.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

print(f"Accuracy: {model.score(X_test, y_test):.2%}")Copy

Types of Machine Learning

ML approaches are categorized by how the model learns from data.

The ML Pipeline

Click each step to learn more about it.

Common Algorithms

y = w₁x₁ + w₂x₂ + ... + wₙxₙ + bCopy

P(y=1|x) = σ(w·x + b) = 1 / (1 + e-(w·x+b))Copy

# Simple neural network in PyTorch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Dropout(0.2),
    nn.Linear(256, 10),
)Copy

Code Examples

Data Preprocessing

import pandas as pd
from sklearn.preprocessing import StandardScaler, LabelEncoder

df = pd.read_csv("data.csv")

# Handle missing values
df.fillna(df.median(numeric_only=True), inplace=True)

# Encode categorical features
le = LabelEncoder()
df["category"] = le.fit_transform(df["category"])

# Scale numerical features
scaler = StandardScaler()
df[numeric_cols] = scaler.fit_transform(df[numeric_cols])Copy

Training with Cross-Validation

from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier

model = GradientBoostingClassifier(
    n_estimators=200,
    learning_rate=0.1,
    max_depth=4,
)

scores = cross_val_score(model, X, y, cv=5, scoring="accuracy")
print(f"CV Accuracy: {scores.mean():.3f} ± {scores.std():.3f}")Copy

Model Evaluation

Best Practices

1. Start Simple

Always establish a baseline with a simple model (logistic regression, decision tree) before trying complex approaches. You need a benchmark to know if complexity is worth it.

2. Avoid Data Leakage

Never let information from your test set influence training. Fit preprocessing (scaling, encoding) on training data only, then transform test data.

# WRONG - fits on all data
scaler.fit(X)

# RIGHT - fits only on training data
scaler.fit(X_train)
X_test_scaled = scaler.transform(X_test)Copy

3. Use Cross-Validation

A single train/test split can be misleading. Use k-fold cross-validation (typically k=5 or k=10) for a more reliable estimate of model performance.

4. Monitor for Overfitting

If training accuracy is much higher than validation accuracy, the model is memorizing noise. Use regularization, dropout, early stopping, or gather more data.

5. Version Everything

Track your data, code, hyperparameters, and model artifacts. Tools like MLflow, DVC, and Weights & Biases help with experiment tracking.

Glossary

Resources

Books

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow — Aurélien Géron
• The Elements of Statistical Learning — Hastie, Tibshirani, Friedman
• Deep Learning — Goodfellow, Bengio, Courville

Libraries