Data science and machine learning notes.

Machine learning chapter notes, model concepts, and practical lessons synced from my learning vault.

Machine Learning

7 notes

A broad overview of machine learning systems, tasks, and the workflow for turning data into useful models.

Practical project workflow notes covering data preparation, validation, model training, and evaluation.

Classification concepts, metrics, decision thresholds, and ways to evaluate model performance.

Core training ideas including regression, gradient descent, regularization, and optimization behavior.

Support vector machines, margins, kernels, similarity features, and related optimization ideas.

Decision tree intuition, splitting criteria, complexity, overfitting, and random forest foundations.

Bagging, boosting, random forests, and ensemble methods for improving model performance.