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Using ML.NET to Build Machine Learning Models

Name: Using ML.NET to Build Machine Learning Models
Price: 59 USD
Author: Fiodar Sazanavets

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.

57 lessons

2 projects

40h

Created by Fiodar Sazanavets

Using ML.NET to Build Machine Learning Models

What's included

57 lessons across 10 modules
8 quizzes with instant feedback
2 projects in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

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

Learning Roadmap

57 Lessons1 Project8 Quizzes

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

7 Lessons

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

Built-In Supervised Learning Tasks in ML.NET

9 Lessons

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

Built-In Unsupervised Learning Tasks in ML.NET

5 Lessons

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

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

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

Conclusion

Implementation of U-Net for Image Segmentation

Project

Premium

10.

Appendix

2 Lessons

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

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Fiodar Sazanavets

Microsoft MVP | senior software engineer | bestselling technical author | software development mentor

Learn more about Fiodar

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