Getting Started
Welcome to the MLNotes documentation. This comprehensive guide covers everything you need to know about machine learning, from basic concepts to advanced implementations.
Quick Start Guide
# Install required packages
pip install torch torchvision numpy pandas scikit-learn
# Import essential libraries
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
# Your first neural network
model = nn.Sequential(
nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 10)
)
Core Concepts
1. Supervised Learning
Supervised learning is the most common paradigm in machine learning. It involves training models on labeled data to make predictions on new, unseen data.
- Classification: Predicting discrete labels
- Regression: Predicting continuous values
- Common algorithms: Decision Trees, SVM, Neural Networks
2. Unsupervised Learning
Unsupervised learning finds hidden patterns in data without labeled examples. It's used for clustering, dimensionality reduction, and anomaly detection.
- Clustering: K-means, DBSCAN, Hierarchical
- Dimensionality Reduction: PCA, t-SNE, UMAP
- Anomaly Detection: Isolation Forest, One-Class SVM
3. Deep Learning
Deep learning uses neural networks with multiple layers to learn complex patterns. It's the foundation of modern AI breakthroughs.
API Reference
Model Training
def train_model(model, dataloader, optimizer, criterion, epochs=10):
"""
Train a PyTorch model.
Args:
model: PyTorch model to train
dataloader: DataLoader with training data
optimizer: Optimizer (e.g., Adam, SGD)
criterion: Loss function
epochs: Number of training epochs
Returns:
Trained model and loss history
"""
model.train()
history = []
for epoch in range(epochs):
total_loss = 0
for batch_x, batch_y in dataloader:
optimizer.zero_grad()
output = model(batch_x)
loss = criterion(output, batch_y)
loss.backward()
optimizer.step()
total_loss += loss.item()
avg_loss = total_loss / len(dataloader)
history.append(avg_loss)
print(f'Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}')
return model, history
Best Practices
Data Preprocessing
- Always normalize/standardize your data
- Handle missing values appropriately
- Split data into train/validation/test sets
- Use data augmentation for small datasets
Model Selection
- Start simple, then increase complexity
- Use cross-validation for robust evaluation
- Monitor for overfitting with validation metrics
- Consider ensemble methods for better performance
Hyperparameter Tuning
- Use systematic search: Grid Search or Random Search
- Consider Bayesian Optimization for efficiency
- Track experiments with tools like MLflow or Weights & Biases
- Don't optimize on the test set!