What you'll learn:
- Explain what artificial intelligence (AI) is and is not, and recognise common misconceptions about intelligent systems.
- Distinguish AI, machine learning and deep learning, and explain how they relate.
- Compare traditional rule-based programming with machine learning that learns relationships from examples.
- Distinguish learning algorithms, trained models, features and labels using everyday examples.
- Compare supervised, unsupervised and reinforcement learning, and identify suitable example problems for each.
- Distinguish classification from regression and interpret a simple numerical prediction.
- Explain how encoding and decoding let machine-learning models work with non-numeric, categorical data.
- Explain function approximators and the idea of neural networks as universal function approximators.
- Describe neurons, layers, network width and depth, and how simple calculations combine in a neural network.
- Calculate a simple neuron's output using inputs, weights, bias and ReLU activation.
- Distinguish choosing a network's structure, training its parameters and using it to make predictions.
- Explain reinforcement-learning agents, environments, actions and rewards, including why reward design matters.
- Describe clustering and dimensionality reduction, with introductory examples of K-means and PCA.
- Compare recommendation-system approaches and explain challenges such as cold starts and limited user data.
- Explain the data preparation, training and evaluation stages demonstrated with Microsoft Model Builder in Visual Studio.
- Explain why fitting training data well is insufficient, and why overfitting and evaluation on new data matter.
- Describe text embeddings and their role in representing meaning for natural language processing (NLP).
- Outline the roles of convolutional networks (CNNs), recurrent networks (RNNs) and transformers at a conceptual level.
- Describe generative AI, large language models (LLMs) and GANs at an introductory level, including their limitations.
- Recognise why convincing AI output still needs checking, using examples of model capabilities, errors and prompting.
- Identify potential AI uses in marketing, customer experience, HR, finance and operations from the business examples.
- Discuss human-AI collaboration, adoption barriers and questions to ask before introducing AI into a team.
- Discuss data quality, privacy, bias, ethics and governance considerations when evaluating AI applications.
- Use AI and machine-learning terminology more precisely in conversations with data scientists and technical colleagues.
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 each model.
Connect the foundations to your work
Explore examples involving marketing, customer experience, human resources, finance, operations and business strategy. Consider human-AI collaboration, data quality, ethics and adoption questions so you can have more informed conversations with technical colleagues and evaluate AI proposals more thoughtfully.
Choose a route through the course
Begin with Sections 1–4 for the foundations and Quizzes 1–4. Continue with Sections 5–6 for learning methods and recommendation systems. Choose Sections 8–11 for the Model Builder demonstrations, or Sections 12–25 for text embeddings and technical applications. Explore the recorded talks, prompting examples and wider business library according to your interests. This is a substantial collection: you can work through the foundations first and return to other topics as your needs develop.
You can watch technical demonstrations to understand the workflow, or reproduce them with the relevant software and background. Model Builder uses Windows and Visual Studio; programming familiarity helps with later code walkthroughs. Some recordings use earlier software versions, so interfaces and features may differ today.
The course combines instructor-created lessons, recorded webinars and talks, and lessons produced with AI assistance, including synthetic narration in selected sections. Together, these formats offer a foundation for further study and a broad library for exploring AI in practice.
