Introduction

To understand machine learning (ML), you need a solid grasp of mathematics, particularly calculus, linear algebra, and statistics. While you can learn ML without a strong math background, it makes understanding the core concepts and algorithms much harder.

There are many free and accessible resources that cover these mathematical topics in detail. This page doesn't provide a in depth introduction but highlights the essential topics to understand before starting with ML. It's okay to learn these topics as you go, but having a basic understanding will make learning ML much easier.

Additionally, basic programming skills are needed for implementing ML algorithms. Python is one of the most commonly used languages in ML, so it's a good idea to learn Python basics if you haven't already.

If you don't have formal training or a background in these topics, don't be discouraged! Many people have successfully transitioned into ML from completely unrelated fields. Regardless of your background, you can learn these concepts with time and practice.

Linear Algebra

• Vectors
• Matrices
• Matrix Multiplication
• Dot Product
• Linear Transformations
• Euclidean Norm

Calculus

• Derivatives
• Partial Derivatives
• Chain Rule
• Convexity
• Gradients

Statistics

• Independence
• Conditional Probability
• Probability Distributions
• Binomial Distribution
• Normal Distribution

Python

• Variables
• Data Types
• Functions
• Flow Control (if statements, loops)
• Data Structures (lists, tuples, dictionaries)
• File I/O
• NumPy
• Basic Object-Oriented Programming (OOP)