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Mathematics

Statistics and Probability
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Statistics and Probability

Statistics, probability, linear algebra, and calculus — the bedrock every ML practitioner needs.

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Linear Algebra
Beginner
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Linear Algebra

Vectors, matrices, and transformations — the geometric language machine learning is built on.

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Calculus & Optimization
Beginner
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Calculus & Optimization

The calculus that trains every model — derivatives, gradients, and the optimization machinery behind gradient descent, backpropagation, and loss minimization.

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Programming

Programming Fundamentals
Beginner
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Programming Fundamentals

Before you write a single line of code, learn what programming actually is — starting from zero, with no assumptions.

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Machine Learning

Machine Learning in Code
Intermediate
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Machine Learning in Code

Build real machine learning models in Python with scikit-learn — preparing features, training, evaluating and tuning — the code form of everything the concept courses taught.

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Deep Learning

Neural Networks & How They Learn
Beginner
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Neural Networks & How They Learn

What a neural network actually is — a neuron, a layer, a depth — and how it learns: measuring how wrong it is, sending that error backwards, and stepping downhill without falling over. Built visually, from zero.

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Python Fundamentals
Beginner
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Python Fundamentals

Learn to read, write, and think in Python — the language you'll use to build real AI — starting from absolute zero.

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Python for AI
Intermediate
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Python for AI

Take the Python you already know and turn it into the toolkit real AI work runs on — collections, files, text, and objects.

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Data with Python
Intermediate
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Data with Python

Work with real data in Python — NumPy arrays, pandas tables, cleaning, summarising and charts — the everyday tools every machine learning project starts with.

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Machine Learning Foundations
Beginner
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Machine Learning Foundations

Machine Learning foundation — how models learn from examples, and where they are applied in the real world.

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Core Machine Learning
Beginner
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Core Machine Learning

Learn the complete supervised + unsupervised machine learning workflow from raw data to trained models.

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Classical Machine Learning
Beginner
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Classical Machine Learning

Master the algorithms and techniques used in real-world tabular machine learning.

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Applied Machine Learning
Beginner
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Applied Machine Learning

Learn how machine learning is applied to different problem domains and deployed in production

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Deep Learning for Images
Intermediate
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Deep Learning for Images

Why ordinary layers fail on pixels, and what convolution does instead — from a grid of numbers to the architectures that made computer vision work.

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Sequences, Language & Attention
Advanced
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Sequences, Language & Attention

From data where order matters to the attention mechanism behind modern language models — recurrence, its limits, and the idea that replaced it.

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Generative & Applied Deep Learning
Advanced
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Generative & Applied Deep Learning

Models that create rather than classify — autoencoders, GANs and diffusion — and what it actually takes to put a deep learning model in front of real users.

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