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
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- 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
Taught by
Tags
On-Demand
