Mathematics for Computing by KodeKloud

Course details

Demystify the math behind the digital world. Build a foundation in linear algebra by learning how to work with vectors, matrices, and tensors that drive graphics and search algorithms. Apply calculus, derivatives, and gradients to see how they fuel machine learning and AI. Interpret probability distributions and Bayes' Theorem for statistical inference and decision-making, and trace how backpropagation trains neural networks for tasks like language translation. Built for aspiring AI and machine learning engineers, data science beginners, and self-taught developers, this course turns abstract formulas into tools you can apply with confidence.

Learning objectives
Apply fundamental concepts of linear algebra, including vectors and matrices, in computing contexts.
Calculate derivatives and interpret gradients to explain how calculus shapes AI learning processes.
Interpret probability distributions and apply Bayes' Theorem to make decisions in uncertain conditions.
Perform matrix operations and use them in practical technology applications.
Apply partial derivatives and gradient descent to optimize machine learning models.

Concepts

Introduction

Course introduction

Linear Algebra

Vectors, matrices, and tensors - Part 1
Vectors, matrices, and tensors - Part 2
Matrix operations - Part 1
Matrix operations - Part 2
Matrix operations - Part 3

Calculus

Derivatives and gradients - Part 1
Derivatives and gradients - Part 2
Partial derivatives and gradient descent - Part 1
Partial derivatives and gradient descent - Part 2
Backpropagation in neural networks

Probability and Statistics

Probability distribution - Part 1
Probability distribution - Part 2
Probability distribution - Part 3
Bayes' theorem and statistical inference