The Ultimate Beginner's Guide to AI and Machine Learning

Every lesson in this course includes a 1 page downloadable resource with the key takeaways from that lesson. We have packaged all of these 1 pagers into a single workbook which you can download in this lesson and keep handy for easy reference as you go through the whole of the course and as you need to refresh these topics in future.

In this lecture we explain the option of downloading the whole course in audio format from this lecture. Once you enrol in the course you will have access to download your zip file from this lecture containing all the lectures in mp3 format.

This lesson is your opportunity to share something about yourself with the rest of the students in this course, and see more about other students and their goals. Tell us all about your goals and what you want to achieve. You can come back to this board and add more thoughts as you go through the course and achieve your goals. Seeing all the other students in the course will also motivate you and keep you going as you participate in this community of learning.

Remember: take action! Achieve your goals, best wishes from your instructor team

Throughout this course we will celebrate your progress at 25%, 50%, 75% and 100%. I really want you to succeed but you need to take action and keep going so look forward to these milestones of progress. I will see you there and cheer you on as you keep going from one milestone to the next >>

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 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, 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.

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

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.

In this video, we are going to start with a very short, theoretical overview of the machine learning process. Thereafter, you will see how to easily add machine learning capability to the class library project that was created in the previous lesson. The Model Builder wizard will be started up, and you will be able to get a visual sense of how the Model Builder wizard automates most of the machine learning processes for you.

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

This video explores important concepts related to evaluating a trained model's performance. The concepts of underfitting and overfitting are covered in a non-technical manner (more technical explanations come later). Thereafter, the remaining steps in the Model Builder process are briefly discussed.

This video outlines the approach that we'll take for the practical machine learning exercise that will take place in this and the following four lessons. Thereafter, it will show you where to find datasets for machine-learning purposes. It will show you where to find the particular dataset that we'll use to train our model. The features and labels in this dataset are explored, along with a very basic overview of the concept of Exploratory Data Analysis. Thereafter, we return to Visual Studio and Model Builder, where we'll load our training data.

This video covers how to load and configure training data within Model Builder. The distinction between model validation and model testing is explored in detail. The important concept of k-fold cross-validation and its use as a technique to mitigate overfitting is also discussed.

This lesson covers the different training options that you can choose from within Model Builder. This includes things like training time and evaluation metrics. The choice of evaluation metrics then leads to a more technical discussion of what it means for a model to be overfitted to training data. The importance of having the optimal evaluation metric for both the training and test data sets is emphasized.

In this video, you finally see the machine learning training process in action. Background processes such as cross-validation, early stopping, and regularization are discussed to explain how Model Builder mitigates overfitting. The trained model is used to predict labels, and some of the limitations of the Model Builder Evaluate GUI are covered.

This video discusses why the trained model predicts some labels better than others. It explores the statistical distribution of the training data set and explains two important regression-related evaluation metrics - R-Squared and RMSE. The impact of these metrics on a trained model's performance is explained.

In this lesson, you will see how to consume your trained model in a console application. You will see how to run your console application inside Visual Studio to make a prediction based on the trained model it is consuming. Thereafter, we train another model using just a training subset of the original data. This model is then consumed in a separate console application. The auto-generated code for the new console application is then extended to test the model against a separate test data set and to determine the evaluation metric for the test data set. We are then able to conclude whether the trained model was overfitted to the training data or not.