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Mathematics for machine learning in python : Linear Algebra, calculus, and statistics for AI and Data science
Mathematics for Machine Learning in Linear Algebra, Calculus, and Statistics for AI and Data Science
Unlock the mathematical foundations of Artificial Intelligence and Data Science with this practical, Python-powered guide. Whether you’re a beginner exploring machine learning or a developer aiming to strengthen your mathematical intuition, this book is your complete roadmap to mastering the essential tools behind today’s AI systems.
Inside, you’ll learn Linear Algebra, Calculus, and Statistics—the three pillars of machine learning—through clear explanations, step-by-step examples, and hands-on coding exercises in Python. From vectors and matrices to derivatives, gradients, probability, and optimization, you’ll discover how mathematical concepts directly power algorithms like Linear Regression, PCA, Gradient Descent, and Neural Networks.
What You’ll
Linear Algebra for AI – Vectors, matrices, eigenvalues, SVD, and their role in machine learning algorithms.
Calculus for Machine Learning – Differentiation, gradients, and optimization techniques like gradient descent.
Statistics & Probability for Data Science – Distributions, Bayes’ theorem, hypothesis testing, and predictive modeling.
Optimization in Python – Cost functions, loss functions, and deep learning optimizers (SGD, Adam, Momentum).
Hands-on Python Projects – Implement PCA, regression models, and neural network training with NumPy, SciPy, and Scikit-learn.
Why This Book?
Designed for students, data scientists, and AI enthusiasts who want to bridge the gap between theory and practice.
Includes real-world applications of mathematics in machine learning, data analysis, and deep learning.
Packed with Python examples so you don’t just learn the math—you implement it.
By the end of this book, you’ll not only understand the mathematical foundations of machine learning but also know how to apply them using Python for AI, deep learning, and data science projects.
If you’re serious about building a strong foundation in machine learning, this book will give you the clarity, confidence, and coding skills you need to succeed.
Unlock the mathematical foundations of Artificial Intelligence and Data Science with this practical, Python-powered guide. Whether you’re a beginner exploring machine learning or a developer aiming to strengthen your mathematical intuition, this book is your complete roadmap to mastering the essential tools behind today’s AI systems.
Inside, you’ll learn Linear Algebra, Calculus, and Statistics—the three pillars of machine learning—through clear explanations, step-by-step examples, and hands-on coding exercises in Python. From vectors and matrices to derivatives, gradients, probability, and optimization, you’ll discover how mathematical concepts directly power algorithms like Linear Regression, PCA, Gradient Descent, and Neural Networks.
What You’ll
Linear Algebra for AI – Vectors, matrices, eigenvalues, SVD, and their role in machine learning algorithms.
Calculus for Machine Learning – Differentiation, gradients, and optimization techniques like gradient descent.
Statistics & Probability for Data Science – Distributions, Bayes’ theorem, hypothesis testing, and predictive modeling.
Optimization in Python – Cost functions, loss functions, and deep learning optimizers (SGD, Adam, Momentum).
Hands-on Python Projects – Implement PCA, regression models, and neural network training with NumPy, SciPy, and Scikit-learn.
Why This Book?
Designed for students, data scientists, and AI enthusiasts who want to bridge the gap between theory and practice.
Includes real-world applications of mathematics in machine learning, data analysis, and deep learning.
Packed with Python examples so you don’t just learn the math—you implement it.
By the end of this book, you’ll not only understand the mathematical foundations of machine learning but also know how to apply them using Python for AI, deep learning, and data science projects.
If you’re serious about building a strong foundation in machine learning, this book will give you the clarity, confidence, and coding skills you need to succeed.
282 pages, Kindle Edition
Published September 7, 2025
2 people are currently reading
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