Using ML.NET to Build Machine Learning Models for AI Solutions.png

mlnet.tar.gz

Command Line

Model Execution

In this course, you will learn how to use ML.NET, which is a tool based on .NET architecture. It consists of a library and command line utility used for building machine learning models. It is so convenient to work with that a developer having little or no background in machine learning and data science can use it to build complex machine learning models.

You will start with an overview of the key ML.NET features and the fundamentals of machine learning. Then, you will go through all types of built-in tasks supported by ML.NET, followed by more advanced ML.NET capabilities, such as deep learning and interoperability with external tools, such as TensorFlow.

By the end of the course, you will be able to use ML.NET to build and train models capable of performing a wide range of machine learning tasks. You will be able to use all the key features of ML.NET and fully integrate it into your apps.

Using ML.NET to Build Machine Learning Models

Which type of ML requires labeled data?

Which type of ML can be used to train AI to play a video game?

Which type of ML can be used to train AI to play a video game?

Which task falls under supervised learning?

Which task falls under supervised learning?

Which task falls under unsupervised learning?

Which task falls under unsupervised learning?

Which is _not_ a component of reinforced ML?

Which is not a component of reinforced ML?

Quiz

Quiz

Test your knowledge of machine learning fundamentals.

Introduction to ML.NET

Machine Learning Fundamentals

Selecting a Problem for Machine Learning

Built-In Supervised Learning Tasks in ML.NET

Built-In Unsupervised Learning Tasks in ML.NET

Deep Learning and Neural Networks

Automating Machine Learning Tasks with AutoML

Saving and Consuming Machine Learning Models

Building an Image Analysis Application

Wrapping Up

Appendix

Quiz Yourself on Machine Learning Fundamentals