Mathematics of Machine Learning
by Tivadar Danka
Why Read This Book?
- •Build a solid foundation in the core math concepts driving AI and ML.
- •Learn linear algebra, calculus, and probability with practical examples.
- •Perfect for data scientists, engineers, and students in Kenya.
- •Designed to make complex math approachable and application-focused.
- •Essential guide for creating and understanding machine learning algorithms.
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Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Probability for Machine Learning by Tivadar Danka, with a foreword by Santiago Valdarrama, is a practical guide designed to help readers understand the mathematical foundations behind modern artificial intelligence.
Originating from the global machine learning education community, the book breaks down complex mathematical concepts such as vectors, matrices, derivatives, gradients, and probability distributions into clear explanations that are directly connected to machine learning algorithms.
Instead of treating mathematics as abstract theory, the book shows how these principles power real AI systems used in data science, neural networks, and predictive models.
Ideal for students, developers, and AI enthusiasts, this book helps readers build the strong mathematical intuition required to truly understand how machine learning models work.
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