Practical AI and Machine Learning with Model Builder AutoML

Master machine learning by doing it in practice, using an automated machine learning GUI that requires little/no coding.

Created by: Irlon Terblanche
CEO at SioTech

Created by: Peter Alkema
Business | Technology | Self Development

What Will I Learn?

• See an end-to-end, supervised machine learning process to tackle a regression problem, using Microsoft's Model Builder and ML .Net.
• Understand the tasks and activities that take place behind the scenes. From data preparation all the way to model training and evaluation.
• Understand data transformation, feature scaling, iterating through algorithms, evaluation metrics, overfitting, cross-validation and regularization.
• Understanding the impact of evaluation metrics on model performance, and how to check for overfitting.
• Understand the lasting fundamentals of machine learning that are independent of the tools or platforms one can use.
• Gain a deep understanding of machine learning concepts by seeing them in action, during a practical machine learning demonstration.
• Understand the importance of Exploratory Data Analysis (EDA) and the impact that the statistical distribution of the data has on model performance.
• Learn how to set up Visual Studio and to configure it to enable Model Builder, the graphical tool that will be used to demonstrate the machine learning process.
• Learn how to use Model Builder to train models without having to code.

Requirements

• A basic understanding of supervised machine learning is required. The student would at the very least need to understand what regression is, what features are, and what it means for a model to be trained to fit a function to input features in order to predict labels.
• The student needs to have a Windows machine with a few GB of free disk space to install Visual Studio, in order to replicate the machine learning process I will demonstrate. However, this is not essential.
• A Windows machine is ideal, but a student with a Mac will still be able to follow along. The course content is visual enough to demonstrate the concepts, without the student having to physically do the machine learning exercise.

Target audience

• This course is for entry-level machine learning enthusiasts, who have had some kind of theoretical introduction to machine learning, but who wants to put the theory into practice.
• Machine learning enthusiasts who do not have a background in Statistics, Data Science or programming, but who want to see the complexities of machine learning in practice.
• Machine learning enthusiasts who want to learn about complex concepts by seeing them in action, rather than by seeing a presentation.
• Technical beginners who want to learn solid machine learning fundamentals before progressing onto more advanced courses where a detailed knowledge of statistics, calculus and programming may be required.