Books in this cluster cover machine learning, data analysis, technical interviews, algorithms and practical engineering reasoning. The emphasis is on concepts, visual intuition, reproducible workflows and real-world systems.
This hub is generated from the controlled subject metadata of the public books library. It connects closely related titles with descriptive internal links and a stable crawlable URL.
A visual decision-tree guide covering entropy, Gini, information gain, ID3, C4.5, CART, pruning, Python, interpretability, projects, and interview preparation.
Decision trees, Entropy, Gini impurity, Information gain
A visual guide to linear regression covering intuition, least squares, diagnostics, regularization, Python, real-world case studies, and model interpretation.
Linear regression, Least squares, Residuals and error, Model assumptions
A visual, interview-focused machine-learning guide with 250 questions covering ML foundations, algorithms, math intuition, model evaluation, feature engineering, deep learning, recommendation, time series, production ML and real-world system reasoning.
Machine learning interview preparation, Probability and statistics, Linear algebra and optimization intuition, Classification and regression
A visual SQL guide from relational foundations and joins to CTEs, window functions, interview problems, business projects, optimization, dashboards, and portfolio work.
Relational databases, SELECT and filtering, Joins, Aggregation