Practical AI and Machine Learning with Model Builder AutoML

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 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.

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.

This is the opening to a quarterly gathering of the Institute of Risk Managers in South Africa. At this forum, one of our instructors, Irlon Terblanche, was asked to discuss Generative AI and its potential implications for Risk Management Professionals. Even though the audio was enhanced to remove background noise and echo, it is not as good as we would have liked. We have attached the actual presentation to supplement the videos, and to help you get through the lessons.

If you don't see a video in this lesson yet, it is only because Udemy is still busy processing it for approval. It should be available soon.