Learn about the prerequisite mathematics for applications in data science and machine learning.

Aug 29, 2025 · Mathematics is the foundation of machine learning. Math concepts play an important role in understanding how models learn from data and optimizing their performance.

Jul 14, 2025 · Master the essential math for ML: linear algebra, calculus, and statistics. Top courses to understand the theory behind neural networks and debug models effectively.

We focus on applied math concepts tailored specifically for machine learning — linear algebra, calculus, probability, and optimization — all explained in context with real ML models and …

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Many of you want to work in machine learning, but the maths skills needed may seem overwhelming. I am here to tell you that it’s nowhere as intimidating as you may think and to …

This collection is far from exhaustive but it should provide a good foundation to start learning some of the mathematical concepts used in machine learning. Reach out on Twitter if you …

Behind every ML success there is Mathematics. All ML models are constructed using solutions and ideas from math. The purpose of ML is to create models for understanding thinking. If you …

Our Mathematics for Machine Learning course provides a comprehensive foundation of the essential mathematical tools required to study machine learning. This course is divided into …

Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, …