The Ultimate Beginner's Guide to AI and Machine Learning

This lesson introduces AI and machine learning through everyday examples. Focus on the main idea: machines can learn patterns from data and use them to perform useful tasks.

A few clarifications for this introductory recording (updated September 2026):

• References to May 2023 describe when the lesson was recorded.

• The image-recognition example uses labelled pictures. Other methods can also learn useful patterns from unlabelled pictures.

• ChatGPT's original training used a combination of methods, including learning from examples and reinforcement learning from human feedback.

• In reinforcement learning, the machine learns from experience and rewards. People still set up the task and the feedback; “learning on its own” does not mean there is no data or human involvement.

You do not need the technical details yet. The following lessons build these ideas step by step. When judging an AI system, ask what it can do reliably and where its answers need checking.

Optional background:

https://openai.com/index/chatgpt/

https://ai.meta.com/blog/dino-v2-computer-vision-self-supervised-learning/

https://spinningup.openai.com/en/latest/spinningup/rl_intro.html

In this lesson, I discuss why programmes that are created through a machine-learning process, are radically different to programmes that have been developed in the classical or traditional way. Machine learning turns classical software development on its head. In this lesson, you'll begin to understand why.

In this lesson, I go into more detail about how machine learning completely turns the classical software development process on its head. And to effectively illustrate this, I start touching on some of the mathematics that replaces conventional programming code.

In this lesson, I go a bit further with the mathematics and function-approximation concepts behind machine learning. This is necessary because it paves the way toward understanding the role that neural networks play in Machine Learning. So, I touch on artificial neural networks in this lesson as well. I also introduce the concepts of encoding and decoding between numeric and non-numeric data such categorical or image data. Machines and Mathematics work with numbers. The ability to encode non-numeric data into something that a machine can understand is therefore a crucial concept to understand.

In this lesson, I discuss the 3 main machine learning techniques. These are Supervised, Unsupervised and Reinforcement Learning techniques. And to clearly explain the differences between these learning techniques, I carefully introduce important and fundamental concepts such as algorithms, models, features, labels, reinforcement learning agents, and rewards.

See how deep neural networks fit into supervised, unsupervised and reinforcement learning. I introduce the network diagram, including neurons, connections and hidden layers, and explain what network width and depth refer to. The lesson approaches neural networks as mathematical function approximators. By the end, you will be able to identify the main parts of a network diagram and explain the relationship between deep learning and the learning methods introduced earlier.

Refresh the mathematical idea of a function before examining how neural networks approximate one. Using graphs and the dimensions of a cone, I explain inputs, outputs, and dependent and independent variables. I then connect these ideas to features, labels and learning a relationship from data. By the end, you will be able to interpret simple function notation and explain why function approximation is useful for supervised prediction.

Follow how an artificial neuron combines inputs, weights and a bias, starting with the simple function y = 1.2x + 1. I explain how the ReLU activation function changes the output, extend the example to multiple inputs, and show how several neurons can combine to represent more complex relationships. By the end, you will be able to identify weights, biases and activation functions in a neuron diagram and explain their roles in function approximation.

Bring the neural-network sequence together with a high-level look at what training needs to determine, including weights and biases, alongside design choices such as layers and activation functions. I briefly introduce backpropagation in the context of supervised training and recap how neural networks can be used with different learning methods. By the end, you will be able to explain the purpose of training and distinguish a learning method from the neural-network model it may use. This is a conceptual overview rather than a worked training calculation.

This lesson introduces the powerful role of reinforcement learning and AI-driven personalization in transforming recommendation systems across entertainment, e-commerce, and loyalty programs. By examining real-world examples from Netflix, Spotify, and leading retailers, it highlights how personalized recommendations significantly enhance customer engagement and business profitability.

Here, we introduce the core principles of reinforcement learning or RL, highlighting its focus on long-term outcomes and sequential decision-making. We show why RL is uniquely suited for loyalty programmes by discussing how it can continuously learn and adapt based on user interactions and feedback.

Here, we introduce the core principles of reinforcement learning or RL, highlighting its focus on long-term outcomes and sequential decision-making. We show why RL is uniquely suited for loyalty programmes by discussing how it can continuously learn and adapt based on user interactions and feedback.

This lesson focuses on practical adoption, showcasing how leading companies have attempted to leverage RL in coalition reward programmes, credit card offers, and retail loyalty ecosystems. We discuss the tangible benefits, common pitfalls, and emerging trends in applying RL-based personalization strategies to drive sustained customer engagement.

This video introduces the speakers/specialists, Irlon and Eric. The high-level agenda/script is attached as a PDF. Irlon's responses to the questions outlined in the agenda are summarised in the attached PowerPoint presentation.

This lesson introduces ML.Net and Model Builder, the free graphical tool that we'll use to go through a supervised machine learning process, without the need for any coding. This lesson also outlines the approach that will be taken during the subsequent 13 lessons. It provides a visually detailed explanation of the major components and sequence of topics that will be covered.

This video shows you how to launch Visual Studio and create a basic class library project. In the next lesson, we'll add machine learning capability to this code library. This video also provides explanations about project templates, solutions, dynamically linked library files, and the .Net Framework. Once a project is created, this video provides a summary of the most important windows in the Visual Studio interface:

1. The Solution Explorer Window,

2. The Code Editor Window, and

3. The Output Window.

This video will show you what types of machine learning tasks Model Builder can automate for you. This video covers concepts like classification, regression, and recommendation-type tasks, that machine-learning models are ideally suited for. This video also provides a quick overview of the different computing resources (CPU, GPU, or Cloud) that can be configured for training a model.

This video covers the essential aspects of preparing your data for training. Data transformation concepts such as encoding and feature scaling are explored.

This video covers a few important concepts related to training a model. In particular, it covers algorithms, trainers, and evaluation metrics.