Dive into a comprehensive 21-minute video tutorial on machine learning using ML.NET. Explore the fundamentals of machine learning, its various types, and the overall process. Learn about ML.NET, its benefits, and how to use it effectively. Follow along with a hands-on demo that covers data loading, feature extraction, model building, and evaluation using C# and ML.NET. Gain practical insights into supervised and unsupervised learning, and discover how to leverage the ML.NET Model Builder for streamlined development. Access provided code samples and datasets to enhance your learning experience.
Overview
Syllabus
- Intro
- What is Machine Learning?
- Types of Machine Learning Systems
- Supervised Learning
- Unsupervised Learning
- The Machine Learning Process
- What is ML.NET?
- Benefits of ML.NET
- ML.NET Model Builder
- ML.NET Demo: The data
- ML.NET Demo: Installing ML.NET
- ML.NET Demo: Creating the ML Context
- ML.NET Demo: Loading in the data
- ML.NET Demo: Splitting the data
- ML.NET Demo: Extract Features
- ML.NET Demo: Building the Pipeline
- ML.NET Demo: Create Model and Make Test Predictions
- ML.NET Demo: Evaluate the Model
- ML.NET Demo: Running the Program
- ML.NET Model Builder Demo
- Conclusion
Taught by
Traversy Media
