HomeCoursesUsing ML.NET to Build Machine Learning Models

Intermediate

40h

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Using ML.NET to Build Machine Learning Models
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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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Overview

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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.

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 comma...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

An understanding of machine learning fundamentals

Content

57 Lessons1 Project8 Quizzes

Introduction to ML.NET

7 Lessons

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

6 Lessons

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.

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

9 Lessons

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

5 Lessons

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.

Deep Learning Fundamentals

Using Deep Learning in ML.NET

Image Processing

Text Processing

Quiz Yourself on Deep Learning

Challenge: Consuming Sentence Similarity Model

Solution Review: Consuming Sentence Similarity Model

Automating Machine Learning Tasks with AutoML

7 Lessons

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

AutoML Overview

Building an AutoML Pipeline

Configuring an AutoML Experiments

Configuring Runner Monitor

Quiz Yourself on AutoML

Challenge: Configuring a Custom AutoML Monitor

Solution Review: Configuring a Custom AutoML Monitor

Saving and Consuming Machine Learning Models

6 Lessons

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

Retraining and Saving an ML.NET Model

Working with ONNX Models

Working with TensorFlow Models

Quiz: Saving and Consuming Machine Learning Models

Challenge: Retraining a Machine Learning Model

Solution Review: Retraining a Machine Learning Model

Building an Image Analysis Application

Project

Wrapping Up

1 Lessons

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

Conclusion

Implementation of U-Net for Image Segmentation

Project

10.

Appendix

2 Lessons

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

Setting up ML.NET Locally

Setting Up ML.NET Locally via Model Builder

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Fiodar Sazanavets

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