The Ultimate Beginner's Guide to AI and Machine Learning

About this course

Build a clear foundation in artificial intelligence and machine learning, even if you are starting without a technical background.

What is AI, and what is it not? How does learning from examples differ from writing rules? What does a neural network actually calculate? This course helps you work through these questions, recognise common misconceptions and use AI terminology with greater confidence.

Start with the ideas behind the technology

Explore AI, machine learning, deep learning, supervised learning, unsupervised learning and reinforcement learning. Make sense of algorithms, models, features and labels, then connect these terms to everyday examples of prediction and decision-making.

The neural-network foundations explain functions and function approximation, encoding and decoding, and the roles of weights, bias and activation functions. Work through simple neuron calculations and distinguish network structure, training and prediction. Four shorter neural-network lessons and the first four scenario-based quizzes help you check your understanding, with explanations for every answer choice.

A beginner entry point, with room to go deeper

No prior programming experience or advanced mathematics is needed to begin the foundational lessons. Everyday examples, diagrams and simple calculations help explain what the mathematics means and why it matters. You do not need to feel confident in mathematics before starting.

The wider course includes technical demonstrations, code walkthroughs, conceptual overviews and business discussions at different levels of depth. The Microsoft Model Builder sequence demonstrates an AutoML workflow in Visual Studio. Further material explores recommendation systems, text embeddings, transformers, generative AI and large language models, alongside introductory overviews of CNNs, RNNs and GANs. These architecture overviews develop conceptual understanding; they do not provide step-by-step projects for building eac

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