Using ML.NET to Build Machine Learning Models

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.

Intermediate

57 Lessons

40h

Certificate of Completion

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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Explanations

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This course includes

1 Project

45 Playgrounds

8 Quizzes

This course includes

1 Project

45 Playgrounds

8 Quizzes

Course Overview

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 task...Show More

What You'll Learn

An understanding of machine learning fundamentals

The ability to use ML.NET to perform a wide range of machine learning tasks

In-depth knowledge of supervised and unsupervised machine learning

Familiarity with deep learning and its implementation using ML.NET

Hands-on experience of AutoML and the automatic model building process

What You'll Learn

An understanding of machine learning fundamentals

Course Content

Introduction to ML.NET

Get familiar with ML.NET basics, fundamentals, project structure, model building, and customization.

Getting Started

ML.NET Fundamentals

Building Our First Machine Learning Model

ML.NET Project Structure

Quiz Yourself on ML.NET Fundamentals

Challenge: Adding Fields to a Machine Learning Model

Solution Review: Adding Fields to a Machine Learning Model

Machine Learning Fundamentals

Grasp the fundamentals of training, categories, and applications of machine learning models.

Training a Machine Learning Model

Categories and Tasks of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Quiz Yourself on Machine Learning Fundamentals

Selecting a Problem for Machine Learning

Examine problem selection, model accuracy, supervised and unsupervised tasks, and improving performance.

Choosing a Task for Machine Learning

Evaluating Model Accuracy

Using Built-In Supervised Learning Tasks

Using Built-In Unsupervised Learning Tasks

Quiz Yourself on Selecting a Problem for Machine Learning

Challenge: Improving Model Accuracy

Solution Review: Improving Model Accuracy

Built-In Supervised Learning Tasks in ML.NET

Apply your skills to supervised learning tasks with ML.NET for binary, multiclass, regression, ranking, and more.

Binary Classification

Multiclass Classification

Quiz Yourself on Supervised Learning

Challenge: Performing a Multiclass Classification

Solution Review: Performing a Multiclass Classification

Built-In Unsupervised Learning Tasks in ML.NET

Solve problems in anomaly detection, clustering, and analyzing clustered data using ML.NET.

Anomaly Detection

Clustering

Quiz Yourself on Unsupervised Learning

Challenge: Analyzing Clustered Data

Solution Review: Analyzing Clustered Data

Deep Learning and Neural Networks

7 Lessons

Tackle deep learning fundamentals, ML.NET integration, image and text processing, and practical coding challenges.

Automating Machine Learning Tasks with AutoML

7 Lessons

Approach automating ML tasks with AutoML, building pipelines, and configuring custom monitors.

Saving and Consuming Machine Learning Models

6 Lessons

Step through retraining and saving ML.NET models, and using ONNX and TensorFlow formats.

Building an Image Analysis Application

Project

Wrapping Up

1 Lesson

Discover the logic behind essential ML.NET skills for practical project applications.

10.

Appendix

2 Lessons

Go hands-on with setting up ML.NET locally and using Model Builder in Visual Studio.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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