Every ML beginner hits the same wall: you know what you want to do, but you can't remember the exact syntax.

Is it fit_transform or transform? Which scaler do I use for outliers? How do I plot a confusion matrix again?

I got tired of Googling the same things, so I built a repo that has all of it in one place.

🔗 github.com/Kavi274/python-ml-cheatsheets

What's Inside

9 cheat sheets covering the full ML workflow:

1. NumPy

Arrays, indexing, broadcasting, linear algebra — the foundation of everything.

 python a = np.array([[1, 2, 3], [4, 5, 6]]) a[a > 3] # [4, 5, 6] a.reshape(3, 2) np.dot(A, B) # matrix multiplication 2. Pandas DataFrames, groupby, merge, datetime, string ops. df.groupby("city").agg({"sales": "sum", "profit": "mean"}) df.isnull().sum() pd.merge(df1, df2, on="id", how="left") 3. Scikit-learn Every major model, pipelines, cross-validation, and hyperparameter tuning. pipe = Pipeline([ ("scaler", StandardScaler()), ("model", RandomForestClassifier()) ]) pipe.fit(X_train, y_train) 4. Matplotlib & Seaborn Every plot type with copy-paste code — histograms, heatmaps, ROC curves, feature importance. sns.heatmap(df.corr(), annot=True, fmt=".2f", cmap="coolwarm") 5. Data Preprocessing Missing values, scaling, encoding, class imbalance — the full cleaning pipeline. num_pipeline = Pipeline([ ("impute", SimpleImputer(strategy="median")), ("scale", StandardScaler()) ]) 6. Feature Engineering Datetime features, target encoding, cyclical encoding, PCA, SHAP-based selection. df["month_sin"] = np.sin(2 * np.pi * df["month"] / 12) df["month_cos"] = np.cos(2 * np.pi * df["month"] / 12) 7. Model Evaluation ROC, precision-recall curves, confusion matrix, optimal threshold, SHAP explainability, calibration. fpr, tpr, _ = roc_curve(y_test, y_prob) roc_auc = auc(fpr, tpr) 8. Deep Learning (Keras) CNNs, RNNs, transfer learning, callbacks, tf.data pipelines. base_model = keras.applications.EfficientNetB0( include_top=False, weights="imagenet" ) base_model.trainable = False 9. Statistics for ML Distributions, hypothesis tests, A/B testing, Bayes' theorem. stat, p = stats.ttest_ind(group1, group2, equal_var=False) print(f"p={p:.4f} — {'Significant' if p < 0.05 else 'Not significant'}") --- How to Use It No setup. No notebooks to run. Every cheat sheet is plain Markdown — just open it on GitHub and hit Ctrl+F. Clone it locally if you want it offline: git clone https://github.com/Kavi274/python-ml-cheatsheets.git --- What's Next Planning to add: - PyTorch - XGBoost / LightGBM - Time Series (Prophet, statsmodels) - HuggingFace Transformers PRs are welcome — if you know a topic well, add a cheat sheet! --- If this saves you even one Google search, consider leaving a ⭐ on GitHub — it helps others find it. 🔗 https://github.com/Kavi274/python-ml-cheatsheets