Machine Learning Cheat Sheet : A Step-by-Step Guide
This cheat sheet distills the entire machine learning workflow into a clear, repeatable sequence you can follow for almost any project.
The Machine Learning Workflow
| Step | What to Do |
|---|---|
| 1. Define the Problem | Is it classification, regression, or clustering? |
| 2. Collect Data | Gather data relevant to the problem |
| 3. Explore & Clean | Handle missing values, outliers, and inconsistencies |
| 4. Feature Engineering | Select, transform, and scale relevant features |
| 5. Split Data | Train/validation/test sets |
| 6. Choose a Model | Pick an algorithm suited to the problem type |
| 7. Train the Model | Fit the model on the training data |
| 8. Evaluate | Measure performance on validation/test data |
| 9. Tune Hyperparameters | Optimize using grid search, random search, etc. |
| 10. Deploy & Monitor | Ship the model and track performance in production |
Choosing an Algorithm — Quick Reference
- Predicting a number: Linear Regression, Random Forest Regressor, XGBoost.
- Predicting a category: Logistic Regression, Decision Trees, SVM, Random Forest.
- Finding groups in data: K-Means, Hierarchical Clustering, DBSCAN.
- Working with images/text: CNNs, RNNs, Transformers.
Common Evaluation Metrics
- Classification: Accuracy, Precision, Recall, F1-score, ROC-AUC.
- Regression: MAE, MSE, RMSE, R-squared.
- Clustering: Silhouette Score, Inertia.
Quick Debugging Checklist
- High training error? → Try a more complex model or more features.
- Large gap between train and test error? → You're likely overfitting; add regularization.
- Both errors high? → You're likely underfitting; add complexity or more relevant features.
Key Takeaway: Keep this sequence handy — most ML projects fail not from a wrong algorithm choice, but from skipping data exploration and evaluation steps.
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