ML.NET for Developers Without Any AI Experience

Overview

Explore AI in .NET applications using ML.NET. Learn to build and train ML models for various tasks like detecting laughter, analyzing mood, and predicting code bugs, all with your existing .NET skills.

Syllabus

• Introduction to ML.NET
Overview of ML.NET
Setting up the development environment
Basics of machine learning in the context of .NET
• Understanding ML.NET Workflows
Data loading and preparation
Model building and training
Model evaluation and deployment
• Building and Training Models
Supervised learning fundamentals
Creating a regression model
Creating a classification model
Model tuning with hyperparameters
• Detecting Laughter using ML.NET
Data collection and preprocessing for audio input
Feature extraction for sound analysis
Building and training a laughter detection model
• Mood Analysis with ML.NET
Sentiment analysis basics
Text processing and feature engineering
Building a sentiment analysis model
• Predicting Bugs in Code
Understanding code metrics and features
Data preparation for code analysis
Training a bug prediction model
• Integrating ML Models into .NET Applications
Consuming trained models within .NET applications
Real-time predictions and serving models
Best practices for model deployment and versioning
• Advanced Concepts
Transfer learning with pre-trained models
Customizing ML.NET with custom algorithms
Leveraging GPU computation for training
• Capstone Project
Group project: Choose a real-world problem
Implement an end-to-end ML.NET solution
Deploy and present results
• Conclusion and Next Steps
Recap of key concepts
Resources for further learning
Introduction to the AI community and career paths in AI with .NET

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Subjects

Conference Talks