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

What fundamental component is required for deep learning?

What fundamental component is required for deep learning?

Which is an essential component of an ANN?

Which is an essential component of an ANN?

Strength of the connection between neurons

Offset term that allows the neuron to adjust its output independently of its inputs

Another name for output classes in a classification problem

What are weights in the context of ANN?

The degree to which the model is more likely to recognize specific classes and not other classes

The measure of how many unique classes the model was trained with

What are biases in the context of ANN?

Image classification API doesn't use any external technologies.

Which external deep learning technology is used by ML.NET image classification API?

Which external deep learning technology is used by ML.NET image classification API?

Quiz

Quiz

Test your knowledge of deep learning in ML.NET.

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 Deep Learning