Mathematics For Machine Learning

The math you will need for your machine learning journey.

What you'll learn

People who want to learn the mathematics that drives machine learning models.

Students who are not sure about data science as a career and want to give it a serious try without paying college level tuition

Data scientists who want a refresher in mathematics.

Students who want to have a solid foundation in mathematics to proceed to more advanced machine learning models.

Product managers who want to know how data scientists and machine learning engineers think.

Machine Learning Engineers, who know how to deploy models, but want to know what is actually going underneath the hood of these models.

Requirements

No programming or math experience necessary, foundational concepts are developed from scratch.

Description

This course provides a comprehensive foundation in the mathematical concepts essential for understanding and implementing machine learning algorithms from first principles. Students will explore Linear Algebra, covering vectors, matrices, eigenvalues, and singular value decomposition—critical for data representation and transformations. Multivariable Calculus will focus on gradients, Jacobians, and Hessians, which are fundamental to optimization techniques used in training models.The course also introduces Probability and Statistics, covering key topics such as random variables, probability distributions, expectation, variance, and fundamental statistical inference techniques. Optimization methods, including gradient descent and related algorithms, will be explored to understand how machine learning models learn from data. Additionally, students will develop problem-solving skills by working through mathematical proofs and derivations that underpin these techniques.Throughout the course, students will gain hands-on experience with NumPy and SciPy, leveraging these powerful Python libraries to implement mathematical concepts programmatically. Rather than applying models to real-world datasets, the focus will be on understanding and building the mathematical foundations necessary for machine learning. By the end of the course, students will have the necessary mathematical and computational tools to derive and implement machine learning techniques from scratch, preparing them for deeper study in artificial intelligence and data science, as well as advanced mathematical modeling.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Python Review

Lecture 2 Python Module Part I

Lecture 3 Python Module Part II

Lecture 4 Python Module Part III

Lecture 5 Python Module Part IV

Section 3: Calculus For Machine Learning

Lecture 6 Mathematical Expressions

Lecture 7 Evaluation, Differentiation, and Integration

Lecture 8 Limits and Series Part I

Lecture 9 Limits and Series Part II

Lecture 10 Limits and Series Part III

Lecture 11 Limits and Differentiation

Lecture 12 Limits and Differentiation Part II

Lecture 13 Polynomial Functions

Lecture 14 Common Mathematical Functions

Lecture 15 Interacting with mathematical functions and Multivariate Functions

Section 4: Linear Algebra For Machine Learning

Lecture 16 Introduction to arrays and matrices

Lecture 17 Foundational Concepts In Linear Algebra

Lecture 18 Determinants and Vector Operations

Lecture 19 Vector Norms, Independence, and orthogonal matrices

Lecture 20 Linear Algebra for Statistics and Machine Learning Part I

Lecture 21 Linear Algebra for Statistics and Machine Learning Part II

Lecture 22 Relationship Between Multivariable Calculus and Linear Algebra

Lecture 23 Linear Algebra and Linear Regression

Section 5: Probability and Statistics

Lecture 24 Probability and Statistics Part I

Lecture 25 Probability and Statistics Part II

Lecture 26 Probability and Statistics Part III

Lecture 27 Statistical Distributions Continuous Part I

Lecture 28 Statistical Distributions Continuous Part II

Lecture 29 Statistical Distributions-Discrete

Lecture 30 The Multivariate Normal Distribution

Lecture 31 Asymptotic Theory

Lecture 32 Sampling Part 1

Lecture 33 Sampling Part 2

Lecture 34 Inferential and Descriptive Statistics Part I

Lecture 35 Inferential and Descriptive Statistics Part II

Lecture 36 Inferential and Descriptive Statistics Part III

Lecture 37 Maximum Likelihood Estimation

Section 6: Optimization

Lecture 38 Optimization with Gradient Descent

Lecture 39 Optimization Newton and Quasi Newton Part I

Lecture 40 Optimization Newton and Quasi Newton Part II

Anybody who wants to understand the mathematics behind machine learning models.,Students, who are not sure if data science is a viable career for them.