Delve into ML.NET to build and train models for various machine learning tasks. Explore key features, advanced capabilities like deep learning, and integration with TensorFlow.

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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

Data preprocessing &rarr; model training &rarr; model deployment &rarr; model evaluation

Data preprocessing &rarr; model training &rarr; model evaluation &rarr; model deployment

Model deployment &rarr; data preprocessing &rarr; model training &rarr; model evaluation

Data preprocessing &rarr; model deployment &rarr; model training &rarr; model evaluation

What is the correct sequence of steps in ML?

What is the correct sequence of steps in ML?

It's an interface for the input data object.

It's an interface for the output data object.

It's an interface that represents a set of input data in the format that can be consumed by the training pipeline. 

It's an interface that represents the data used in predictions.

What is the role of `IDataView` in ML.NET?

What is the role of <code>IDataView</code> in ML.NET?

It's an interface that represents an object that transforms input data into output based on a trained model.

It's an interface that represents a component in an ML pipeline.

It's an interface that represents a prediction engine.

It's an interface that abstracts away the whole training pipeline.

What is the role of `ITransformer` in ML.NET?

What is the role of <code>ITransformer</code> in ML.NET?

For a binary classification problem, if you have a dataset with four columns, no header row, and the category label represented by the last column, what parameters would you apply to the command to train the model?

For a binary classification problem, if you have a dataset with four columns, no header row, and the category label represented by the last column, what parameters would you apply to the command to train the model?

It's placed inside a ZIP file that has to be placed next to the autogenerated code.

It's placed inside a ZIP file that can be placed anywhere.

Once the model is trained by ML.NET, how is it stored?

Once the model is trained by ML.NET, how is it stored?

Quiz

Quiz

Test your knowledge of ML.NET 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 ML.NET Fundamentals