"Mathematics for Machine Learning" eBook dives deep into the essential mathematical concepts integral to machine learning. From the basics of linear algebra, analytic geometry, and vector calculus to optimization methods, our book offers a comprehensive foundation. Delving further, it also explores crucial ML problems, discussing model fitting, regression, PCA, and more. A perfect guide for those aiming to excel in ML!
Understanding Linear Algebra in Machine Learning
Discover the role of linear algebra in machine learning including matrix operations, data representation, and linear equations.
Analysing Geometry for Machine Learning Applications
Explore the principles of geometry and how they're applied to machine learning algorithms inside this e-book.
Exploring Matrix Decompositions in Data Handling
Discover the role of matrix decompositions in data handling for enhanced machine learning processes.
Learning Vector Calculus for Optimal Machine Learning
Delve into the math behind machine learning, exploring topics from linear algebra to vector calculus and optimization techniques.
Grasping Probability and Distributions in Machine Learning
Explore the fundamentals of probability theory and distributions in machine learning with this insightful e-book.
Mastering Continuous Optimization in Machine Learning
Dive into the mathematical aspects of machine learning with continuous optimization techniques and foundational theories.
