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ML Cheatsheet — Everything You Need in One Place

Your comprehensive quick reference for machine learning concepts, algorithms, formulas, and best practices. Perfect for interviews and daily work.

Algorithm Summaries — Quick reference for all major ML algorithms
Formula Reference — Mathematical foundations at your fingertips
Best Practices — Proven guidelines for ML projects

"Knowledge is power, but organized knowledge is superpower."

ML Cheatsheet — Quick Reference

A comprehensive quick reference for machine learning algorithms, metrics, math, and Python code.

Algorithm Comparison Chart

Decision Tree: Model Selection

Classification Metrics

DfClassification Metrics

Metrics for evaluating classification models, measuring performance across different aspects like accuracy, precision, recall, and their trade-offs.

Metric	Formula	When to Use
Accuracy	$(TP+TN)/(TP+TN+FP+FN)$	Balanced classes
Precision	$TP/(TP+FP)$	Cost of false positive is high (spam)
Recall	$TP/(TP+FN)$	Cost of false negative is high (cancer)
F1 Score	$2 \cdot \frac{P \cdot R}{P + R}$	Imbalanced classes
AUC-ROC	Area under ROC curve	Ranking quality
Log Loss	$-\frac{1}{N}\sum[y\log p + (1-y)\log(1-p)]$	Probabilistic predictions

Regression Metrics

Metric	Formula	Interpretation
MSE	$\frac{1}{N}\sum(y_i - \hat{y}_i)^2$	Penalizes large errors
RMSE	$\sqrt{MSE}$	Same units as target
MAE	$\frac{1}{N}\sum\\|y_i - \hat{y}_i\\|$	Robust to outliers
R²	$1 - \frac{SS_{res}}{SS_{tot}}$	Variance explained (0-1)
MAPE	$\frac{100\%}{N}\sum\\|\frac{y-\hat{y}}{y}\\|$	Percentage error

Math Quick Reference

Linear Algebra

Dot product: $\mathbf{a} \cdot \mathbf{b} = \sum_i a_i b_i$

Matrix multiply: $(AB)_{ij} = \sum_k A_{ik}B_{kj}$

Norm: $\|\mathbf{x}\|_2 = \sqrt{\sum_i x_i^2}$

Eigenvalue: $A\mathbf{v} = \lambda\mathbf{v}$

Calculus

Power rule: $\frac{d}{dx}x^n = nx^{n-1}$

Chain rule: $\frac{d}{dx}f(g(x)) = f'(g(x)) \cdot g'(x)$

Gradient: $\nabla f = [\frac{\partial f}{\partial x_1}, ..., \frac{\partial f}{\partial x_n}]$

Probability

Bayes' theorem: $P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}$

Expected value: $E[X] = \sum_x x \cdot P(x)$

Variance: $\text{Var}(X) = E[(X-\mu)^2] = E[X^2] - (E[X])^2$

Normal distribution: $f(x) = \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}$

Python Libraries

Data: pandas, numpy
Visualization: matplotlib, seaborn, plotly
ML: scikit-learn, xgboost, lightgbm
Deep Learning: pytorch, tensorflow, keras
NLP: transformers, spacy, nltk
CV: opencv, torchvision
AutoML: auto-sklearn, optuna
Deployment: fastapi, flask, streamlit
Experiment: mlflow, wandb

Key Takeaways

Summary: ML Cheatsheet

Start simple — linear models as baselines before complex ones
Feature engineering matters more than algorithm choice
Cross-validate everything — never trust a single train/test split
Regularize to prevent overfitting (L1 for sparsity, L2 for smoothness)
Scale features for distance-based algorithms (KNN, SVM, K-Means)
Ensemble multiple models for best performance (bagging, stacking)
Monitor models in production for data drift and performance degradation
Keep learning — the field evolves fast (new papers every week)

What to Learn Next

-> What is Machine Learning? — Complete Introduction Learn about what is machine learning? — complete introduction.

-> Linear Regression — Complete Guide with Math and Code Learn about linear regression — complete guide with math and code.

-> Model Evaluation — Metrics, Cross-Validation and Selection Learn about model evaluation — metrics, cross-validation and selection.

-> Transformers — Attention Is All You Need Complete Guide Learn about transformers — attention is all you need complete guide.

-> ML System Design — Architecture and Production Patterns Learn about ml system design — architecture and production patterns.

-> ML Interview Prep — Questions, Answers and System Design Learn about ml interview prep — questions, answers and system design.

ML Cheatsheet — Quick Reference Guide