Mathematics for Data Science and Artificial Intelligence

This book provides a comprehensive foundation in the mathematical tools essential for modern data science and machine learning. It blends core subjects such as linear algebra calculus probability statistics optimization and numerical methods with real-world applications. Readers explore matrix operations eigenvalues and dimensionality reduction techniques like PCA and t-SNE. Optimization is covered through gradient-based methods and regularization strategies. Probability theory Bayes' theorem and statistical inference form the basis for modeling uncertainty. Information theory concepts like entropy cross-entropy and KL divergence are applied to learning and feature selection. Efficient computational methods are introduced using Python/Numpy implementations. Advanced topics include graph theory for network analysis and stochastic models such as Markov chains and ARIMA for time series forecasting. This book bridges theory and practice offering step-by-step problem-solving coding exercises and a deep understanding of the mathematical backbone driving AI and data science.