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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.
57 Lessons
2 Projects
40h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
1.
Introduction to ML.NET
Introduction to ML.NET
Get familiar with ML.NET basics, fundamentals, project structure, model building, and customization.
2.
Machine Learning Fundamentals
Machine Learning Fundamentals
Grasp the fundamentals of training, categories, and applications of machine learning models.
3.
Selecting a Problem for Machine Learning
Selecting a Problem for Machine Learning
7 Lessons
7 Lessons
Examine problem selection, model accuracy, supervised and unsupervised tasks, and improving performance.
4.
Built-In Supervised Learning Tasks in ML.NET
Built-In Supervised Learning Tasks in ML.NET
9 Lessons
9 Lessons
Apply your skills to supervised learning tasks with ML.NET for binary, multiclass, regression, ranking, and more.
5.
Built-In Unsupervised Learning Tasks in ML.NET
Built-In Unsupervised Learning Tasks in ML.NET
5 Lessons
5 Lessons
Solve problems in anomaly detection, clustering, and analyzing clustered data using ML.NET.
6.
Deep Learning and Neural Networks
Deep Learning and Neural Networks
7 Lessons
7 Lessons
Tackle deep learning fundamentals, ML.NET integration, image and text processing, and practical coding challenges.
7.
Automating Machine Learning Tasks with AutoML
Automating Machine Learning Tasks with AutoML
7 Lessons
7 Lessons
Approach automating ML tasks with AutoML, building pipelines, and configuring custom monitors.
8.
Saving and Consuming Machine Learning Models
Saving and Consuming Machine Learning Models
6 Lessons
6 Lessons
Step through retraining and saving ML.NET models, and using ONNX and TensorFlow formats.
10.
Appendix
Appendix
2 Lessons
2 Lessons
Go hands-on with setting up ML.NET locally and using Model Builder in Visual Studio.
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Developed by MAANG Engineers
ABOUT 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
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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