In this course, you will get to understand the foundational concepts that underlie the supervised machine-learning process. You will get to understand complex topics such as:

• Exploratory Data Analysis,

• Data Transformation and Feature Scaling,

• Evaluation Metrics, Algorithms, trainers, and models,

• Underfitting and Overfitting,

• Cross-validation, Regularization, and much more

You will see these concepts come alive by doing a practical machine-learning exercise, rather than by looking at presentations. We will be using a non-cloud-based machine-learning tool called Model Builder, inside of Visual Studio. There will be zero coding involved (except for the very last lesson). But even though there is little coding involved, you will still get a very detailed understanding of complex machine-learning concepts.

This course requires you to have at least some theoretical exposure to the concepts of supervised and unsupervised machine learning. This course is designed to build on a basic, theoretical understanding of machine learning by doing a practical machine-learning exercise. The concepts taught in this course are foundational and will be relevant in the future, regardless of what machine learning platform or programming language you use.

In the process, you will also get some exposure to Visual Studio, code projects, solutions, and the Microsoft Machine Learning ecosystem. But that is just a side benefit. This course focuses on machine learning itself, not the tools that are used.

If you've already done any kind of machine learning or trained a model, this course might be too basic for you. This course may contain foundational knowledge that you may not have been taught before, but please be aware that this course is geared toward beginner and intermediate-level AI enthusiasts.