Practical Guide to AI & ML: Mastering Future Tech Skills

In this lesson, I introduce AI at a high level. I slowly introduce the fact that AI systems such as ChatGPT and Bard do not yet have human-like intelligence. I also introduce the topic of machine learning and how this is different to AI.

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.

In this lesson, I discuss Deep Learning, Deep Neural Networks and mathematical function approximation. This lesson focusses on how neural networks can approximate the functions and models used by AI systems. But it does not cover the training of the neural network. Those are advanced topics for future lessons.

In this lesson, we contrast traditional rule-based logic (If-Then-Else) with ML’s data-driven approaches. This lesson sets the stage by explaining why math underlies all of machine learning. Whether it is supervised or unsupervised learning, the “model” is a mathematical function being optimised. This lesson explains the basics of how the different supervised and unsupervised machine learning algorithms ‘fit a function to known data’, so that after it is ‘trained’ it can be used on new data. This is in stark contrast to traditional programming where we rely on hard-coded logic.

We introduce the concept of a model as a function that takes inputs (features) and produces predictions (regression, classification, etc.). We clarify what model parameters are, and how fitting a model means adjusting these parameters to better approximate the mapping between inputs and outputs. Additionally, we ensure a proper understanding of important concepts such as labelled data, algorithms, loss functions, overfitting and model performance evaluation.

We introduce the concept of a model not as a function that predicts outputs for inputs, but instead organizes data based on relationships between the data items. We clarify what model parameters are, and how ‘fitting a model’ means adjusting these parameters to better approximate the underlying data relationships. Additionally, we ensure proper understanding of important concepts such as unlabelled data, algorithms, loss functions, overfitting and model performance evaluation.

This lesson introduces fascinating real-world examples of where reinforcement learning (RL) is powering the most mind-blowing advancements of our time. This includes, but is not limited to robotics, game-playing agents, landing rockets, controlling nuclear fusion reactions, high-frequency algorithmic trading and advanced recommender systems (like Netflix or Instagram).

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.

This lesson outlines the fundamentals of traditional recommender systems, including collaborative filtering, matrix factorization, and content-based techniques. It explores how these methods evolved to address user preferences in loyalty and rewards programmes, paving the way for more advanced approaches.

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 outlines how to download, install, and configure Visual Studio. If you already have Visual Studio running on your machine, you can configure it for Machine Learning in one of two ways:

1. Via the Visual Studio Installer, ensure that you have the correct workload. Alternatively,

2. Add the Model Builder Extension from inside Visual Studio.

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.