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
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Content
1.
Introduction to ML.NET
7 Lessons
Get familiar with ML.NET basics, fundamentals, project structure, model building, and customization.
2.
Machine Learning Fundamentals
6 Lessons
Grasp the fundamentals of training, categories, and applications of machine learning models.
3.
Selecting a Problem for Machine Learning
7 Lessons
Examine problem selection, model accuracy, supervised and unsupervised tasks, and improving performance.
4.
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.
5.
Built-In Unsupervised Learning Tasks in ML.NET
5 Lessons
Solve problems in anomaly detection, clustering, and analyzing clustered data using ML.NET.
6.
Deep Learning and Neural Networks
7 Lessons
Tackle deep learning fundamentals, ML.NET integration, image and text processing, and practical coding challenges.
7.
Automating Machine Learning Tasks with AutoML
7 Lessons
Approach automating ML tasks with AutoML, building pipelines, and configuring custom monitors.
8.
Saving and Consuming Machine Learning Models
6 Lessons
Step through retraining and saving ML.NET models, and using ONNX and TensorFlow formats.
9.
Wrapping Up
1 Lessons
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.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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