AI & Machine Learning Cheat Sheet

Free AI & Machine Learning cheat sheet: the most-used AI & Machine Learning syntax and methods at a glance — searchable and beginner-friendly.

Key Concepts

Concept	Syntax	Example
Supervised learning	`Labeled data → model`	`Classification, Regression`
Unsupervised learning	`Unlabeled data → patterns`	`Clustering, Dimensionality reduction`
Reinforcement learning	`Agent → actions → rewards`	`Game AI, Robotics`
Transfer learning	`Pre-trained model → fine-tune`	`Use BERT for your NLP task`

Common Algorithms

Concept	Syntax	Example
Linear Regression	`y = wx + b`	`Predicting house prices`
Decision Tree	`if-else rules from data`	`Customer churn prediction`
K-Nearest Neighbors	`Classify by nearest points`	`Image classification`
Neural Network	`Layers of weighted nodes`	`Deep learning, NLP`
Random Forest	`Ensemble of decision trees`	`Robust classification/regression`

Python for ML

Concept	Syntax	Example
NumPy array	`np.array([1, 2, 3])`	`import numpy as np`
Pandas DataFrame	`pd.DataFrame(data)`	`df = pd.read_csv('data.csv')`
Train/test split	`train_test_split(X, y)`	`from sklearn.model_selection import train_test_split`
Fit model	`model.fit(X_train, y_train)`	`model = LinearRegression(); model.fit(X, y)`

Data Preparation

Concept	Syntax	Example
Normalization	`(x - min) / (max - min)`	`Scale features to 0-1 range`
Standardization	`(x - mean) / std`	`Scale features to mean=0, std=1`
One-hot encoding	`pd.get_dummies(df['col'])`	`Convert categories to binary columns`
Handle missing	`df.fillna() / df.dropna()`	`df['col'].fillna(df['col'].mean())`

Evaluation Metrics

Concept	Syntax	Example
Accuracy	`(TP+TN) / Total`	`Overall correctness`
Precision	`TP / (TP+FP)`	`Of predicted positive, how many correct?`
Recall	`TP / (TP+FN)`	`Of actual positive, how many found?`
F1 Score	`2 * (P*R) / (P+R)`	`Harmonic mean of precision & recall`