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.

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 would be a mandatory component of a dataset used for supervised learning?

What would be a mandatory component of a dataset used for supervised learning?

What kind of numeric values are used to determine the accuracy of an ML model?

What kind of numeric values are used to determine the accuracy of an ML model?

Micro-accuracy is based on the mean number of correct predictions while macro-accuracy is based on the median.

Micro-accuracy is taken from the class with the highest accuracy, while macro-accuracy represents the average number of correct answers across the entire dataset.

Micro-accuracy gives equal weight to each data point while macro-accuracy represents an average from accuracy values in each class.

Macro-accuracy gives equal weight to each data point while micro-accuracy represents an average from accuracy values in each class.

What is the difference between micro-accuracy and macro-accuracy?

What is the difference between micro-accuracy and macro-accuracy?

Which technique can be used to retrain the model if you aren’t satisfied with its accuracy?

Which technique can be used to retrain the model if you aren’t satisfied with its accuracy?

If you wanted to forecast how house prices would change in the future, what ML task would you use?

If you wanted to forecast how house prices would change in the future, what ML task would you use?

Quiz

Quiz

Test your knowledge of the process of selecting a problem for a machine learning model to solve.

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 Selecting a Problem for Machine Learning