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.…