Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence

Jorge Brasil

In this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field.As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance.This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios.

Before Machine Learning Volume 2 - Calculus for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence

Jorge Brasil

This book takes readers on a structured journey through calculus fundamentals essential for AI. Starting with “Why Calculus?” it introduces key concepts like functions, limits, and derivatives, providing a solid foundation for understanding machine learning.As readers progress, they will encounter practical applications such as Taylor Series for curve fitting, gradient descent for optimization, and L'Hôpital’s Rule for managing undefined expressions. Each chapter builds up from core calculus to multidimensional topics, making complex ideas accessible and applicable to AI.The final chapters guide readers through multivariable calculus, including advanced concepts like the gradient, Hessian, and backpropagation, crucial for neural networks. From optimizing models to understanding cost functions, this book equips readers with the calculus skills needed to confidently tackle AI challenges, offering insights that make complex calculus both manageable and deeply relevant to machine learning.

Before Machine Learning Volume 3 - Probability and Statistics for A.I . Master Probability, Statistics, and Their Role in AI's Future Evolution

Jorge Brasil

Delve into the importance of probability and statistics in AI, beginning with fundamental measures like mean, median, and variance. This book takes you on a journey through the basics of probability theory, introducing key concepts such as central tendency, variance, and probability distributions. It emphasizes the role of statistical measures in understanding and analyzing data.Building on these foundations, the book explores hypothesis testing, Bayesian inference, and statistical distributions in-depth. Readers will gain practical insights into essential techniques for model evaluation, maximum likelihood estimation, and the interpretation of data in the context of AI applications. Each concept is illustrated with practical examples and case studies to ensure clarity and application.Finally, advanced topics like Markov processes, hierarchical Bayesian models, and multivariate distributions are introduced. The book addresses critical areas like variance, correlation, and hypothesis testing, equipping readers with the skills to tackle real-world challenges in AI and machine learning. Whether you're a student, professional, or AI enthusiast, this book offers the essential statistical tools and knowledge to excel in the field.