Free online courses onMathematics for Machine Learning

Free Mathematics for Machine Learning Courses (AI Foundations)

Build a stronger Artificial Intelligence skill set by mastering the core math behind modern models. This collection of free online courses focuses on the essential mathematical tools used in machine learning, deep learning, and data-driven AI. You will practice concepts that explain why algorithms work, how they learn from data, and how to diagnose or improve performance. Whether you are preparing for advanced AI study or want more confidence reading research papers, these free courses help you develop the mathematical intuition and problem-solving skills that machine learning demands.

Mathematics for Machine Learning connects theory to practical ML workflows: understanding gradients for training, matrix operations for feature transformations, and probability for uncertainty and generalization. By learning these fundamentals, you can better interpret model behavior, choose appropriate methods, and communicate results clearly. Each course is designed to be approachable while still emphasizing the key formulas, definitions, and techniques commonly used across supervised learning, unsupervised learning, and neural networks.

What you will learn across these free courses

• Core ML notation and reasoning: vectors, matrices, functions, and the language used in AI literature
• Optimization foundations: how loss functions are minimized and why gradients matter
• Probabilistic thinking: modeling uncertainty, making predictions, and evaluating outcomes
• Linear transformations: projections, embeddings, and dimensionality reduction concepts
• Practical problem solving: working through examples that mirror real machine learning scenarios

Free courses included in this topic

Probability for Machine Learning

Learn probability concepts that power classification, generative modeling, and evaluation. Topics commonly include random variables, common distributions, expectation and variance, conditional probability, Bayes’ rule, and basic statistical reasoning. You will see how probabilistic modeling supports decision-making under uncertainty and how it relates to metrics, calibration, and risk.

Calculus for Machine Learning

Study the calculus used to train models efficiently. Typical coverage includes limits and derivatives, partial derivatives, gradients, Jacobians, and chain rule concepts that appear in backpropagation. You will also explore optimization ideas such as gradient descent, learning rates, and how curvature intuition can help debug training instability.

Linear Algebra for Machine Learning

Master the linear algebra that underpins feature spaces and neural network layers. Common topics include vector spaces, matrix multiplication, determinants, rank, eigenvalues and eigenvectors, orthogonality, and decompositions used in dimensionality reduction. This course builds the tools needed for understanding embeddings, least squares, and many ML transformations.

Who these free online courses are for

These free Mathematics for Machine Learning courses are ideal for beginners entering AI, developers transitioning into machine learning, and learners who want a clearer understanding of the math behind models. If you can commit to practicing problems and revisiting key definitions, you will gain a durable foundation for deeper Artificial Intelligence learning.

About free courses Mathematics for Machine Learning and Artificial Intelligence

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