Class imbalance happens when one class in your dataset vastly outnumbers another. Learn how to detect it, evaluate it correctly, and choose fixes that match your real costs — not just a 50:50 ratio.

Data leakage is the unintended exposure of sensitive information. This guide explains causes, detection, prevention, and how the term differs in machine learning.

A train-test split divides your data so a model can be judged on examples it has never seen. This guide covers ratios, Python code, stratified and grouped splitting, time-series validation, and the leakage mistakes that quietly ruin evaluation results.

A practical, technically grounded guide to stratified sampling in machine learning — what it is, when to use it, and how to implement it without common mistakes. Covers train-test splits, cross-validation, grouped data, and imbalanced classes.

SMOTE creates synthetic minority-class examples by interpolating between real neighbors, helping classifiers see rare cases more clearly. This guide covers the algorithm, worked math, Python code, evaluation, leakage traps, and honest limitations.

Missing values can quietly wreck an analysis or a model. This guide explains what data imputation is, how MCAR, MAR, and MNAR differ, and how to choose, implement, and evaluate imputation methods in Python and R.

A plain-English, technically grounded walkthrough of sentence embeddings — how transformer models turn sentences into vectors, how similarity is measured, and how the technique powers semantic search and RAG.

Byte-Pair Encoding (BPE) started as a 1994 compression trick and now powers tokenization in GPT, RoBERTa, and most large language models. Here's exactly how it works.

Self-attention treats a sentence like a bag of tokens until positional encoding tells it what came first. This guide covers the math, the major methods, and how they shape long-context models.