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

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments.

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.

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.

This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory.

This course aims to bridge the gap between a thorough knowledge of mathematics and the machine learning methods that are based on it.

Aug 6, 2025 · Machine learning is built upon three pillars: linear algebra, calculus, and probability theory. Here’s the full roadmap for you. Linear algebra is used to describe models, calculus is …

Master the math behind machine learning with intuitive, real-world lessons. From linear algebra to calculus, learn essential concepts through engaging, animated videos and practical exercises.

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 …

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, …