Machine Learning Math Simplified: Free Course Guide

Machine Learning Math Simplified is a step-by-step course on the mathematics behind machine learning, deep learning and AI, taught with intuition first and formulas second. It is for aspiring ML engineers, data science students and developers who already use ML libraries but want to understand the math those libraries rely on.

The course covers scalars, vectors, matrices and tensors, linear algebra and matrix properties, eigenvalues and eigenvectors, matrix decompositions including SVD and PCA, and the calculus behind gradient descent. It has 9 in-depth videos, about 4.8 hours in total, free on YouTube, and listed on the Machine Learning Math course page.

Who this course is for

The course description names these learners:

• Aspiring machine learning engineers
• Data science students
• AI enthusiasts
• Software engineers moving into ML
• Anyone preparing for ML interviews
• Developers who use ML libraries but want to understand the math behind them

The catalog marks it Intermediate. It is described as beginner-friendly but depth-oriented, and it is designed specifically for ML rather than as a general maths course.

What you will learn

1. The roadmap and the building blocks. Machine Learning Mathematics Simplified – Complete Roadmap for ML Foundations shows how the topics connect. Tensors Explained for Machine Learning – Scalars, Vectors, Matrices & Dimensions Made Simple then covers the objects every model works with. If you understand shapes and dimensions, most “shape mismatch” errors stop being mysterious.

2. Linear algebra and tensor operations. Linear Algebra for Machine Learning – Complete Beginner Friendly Guide and Tensor Operations in Machine Learning – Dot Product, Transpose, Hadamard & Broadcasting cover the operations models perform all the time. As the course description puts it, neural networks run on matrix multiplication.

3. Matrix properties. Matrix Properties Every Machine Learning Engineer Must Know covers the properties that decide what you can and cannot do with a matrix. They come up again in every decomposition later on.

4. Eigenvalues and eigenvectors. Eigenvalues & Eigenvectors Explained for Machine Learning – PCA Intuition is the longest video in the course, at about 54 minutes. It builds the intuition you need for PCA, which uses eigenvectors.

5. Decompositions: eigen decomposition, SVD and PCA. Matrix Decomposition & Eigen Decomposition for Machine Learning and SVD, Pseudoinverse & PCA for Machine Learning – Practical Applications show how breaking a matrix apart powers dimensionality reduction and other practical uses.

6. Calculus for learning. Derivatives, Delta Method & Chain Rule for Machine Learning – Gradient Descent Explained covers how models actually learn. Gradient descent relies on derivatives, and backpropagation depends on the chain rule.

How the course is organised

There are 9 videos and about 4.8 hours in total. They are long-form lessons, mostly 28 to 54 minutes, with the roadmap and the linear algebra guide being shorter.

The playlist begins with the calculus video and ends with the roadmap. If maths is not fresh for you, this order builds up more gently:

1. Complete Roadmap for ML Foundations
2. Linear Algebra for Machine Learning – Complete Beginner Friendly Guide
3. Tensors Explained for Machine Learning
4. Tensor Operations in Machine Learning
5. Matrix Properties Every Machine Learning Engineer Must Know
6. Eigenvalues & Eigenvectors Explained for Machine Learning
7. Matrix Decomposition & Eigen Decomposition for Machine Learning
8. SVD, Pseudoinverse & PCA for Machine Learning
9. Derivatives, Delta Method & Chain Rule – Gradient Descent Explained

How to study it step by step

1. Start with the roadmap so you know why each topic is there.
2. Watch one long video per sitting. These are dense, and pausing to work examples on paper is the point.
3. Work small examples by hand. Multiply two 2×2 matrices or transpose a small tensor yourself before you trust a library to do it.
4. Check yourself in code. If you know Python, repeat each operation in NumPy and confirm you get the same answer as on paper.
5. Rewatch the eigenvalues video before the decomposition videos. SVD and PCA are much easier once eigenvectors feel natural.
6. End with the calculus video, then explain gradient descent in your own words. That makes a good interview self-test.

This course currently has no Skills Pragati hands-on lab guide, practice simulation or mock exam.

Frequently asked questions

Is the Machine Learning Math course free?

Yes. All 9 videos are free on YouTube. You can follow them from the course page.

How long does it take?

The videos add up to about 4.8 hours. Because each lesson is long and dense, plan on one or two videos a week if you work the examples, so roughly a month of steady study.

Do I need strong maths to start?

No. The course puts intuition before formulas and includes a beginner-friendly linear algebra guide. School-level algebra is enough to begin. The catalog rates it Intermediate because it goes deep.

How do I practise with real data?

Pair it with Learn Data Analysis From Scratch Using Python, which teaches NumPy, Pandas and plotting. The NumPy lessons there, including broadcasting and vectorisation, are a natural place to try the operations from this course.