Microsoft ML.NET

1. Introduction to ML.NET
  • What is ML.NET and its Role in the .NET Ecosystem
  • Key Concepts: Data Preparation, Training, Evaluation, Prediction
  • ML.NET vs Python-based ML Frameworks
  • ML.NET Architecture and Supported Workloads
  • Tools: Visual Studio, Model Builder, ML.NET CLI
2. ML.NET Tools and Setup
  • Installing ML.NET SDK and Extensions
  • Overview of ML.NET CLI and Model Builder
  • First ML.NET Project: Predict Housing Prices
  • Understanding the MLContext Object
3. Data Loading and Preparation
  • Defining Input and Output Data Schemas
  • Loading Data from CSV, JSON, or Database
  • Data Preprocessing Techniques
  • Using Data Transforms and Pipelines
4. Model Training with ML.NET
  • Supervised Learning in ML.NET
  • Unsupervised Learning (Clustering, Anomaly Detection)
  • Choosing Algorithms and Understanding Trainers
  • Cross-Validation and Hyperparameter Tuning
5. Model Evaluation and Metrics
  • Metrics for Regression: R-Squared, MAE, RMSE
  • Metrics for Classification: Accuracy, AUC, F1 Score
  • Model Comparison and Selection
  • Visualizing Performance (Confusion Matrix, ROC)
6. Predictions and Model Consumption
  • Creating a Prediction Engine
  • Batch vs Real-Time Predictions
  • Using Trained Models in Console, Desktop, or Web Apps
  • Saving and Loading Models for Production Use
7. AutoML and Model Builder
  • Introduction to Automated Machine Learning in ML.NET
  • Using Model Builder for GUI-Based Training
  • Running AutoML Experiments Programmatically
  • Comparing Manual and AutoML Workflows
8. Integrating ML.NET in Applications
  • Embedding ML Models in ASP.NET Core Web APIs
  • Building End-to-End .NET Apps with ML Models
  • Logging, Monitoring, and Versioning Models
  • Deploying ML.NET Models in Production Environments
9. Advanced Scenarios
  • Anomaly Detection (e.g., Fraud, Outliers)
  • Time Series Forecasting with SSA
  • Image Classification with TensorFlow Integration
  • Using ONNX Pre-trained Models with ML.NET
10. Capstone Project
  • Define a Business Problem: e.g., Churn Prediction, Sentiment Analysis
  • Build and Train a Model Using ML.NET
  • Evaluate, Save, and Deploy the Model
  • Create a User Interface or API for Model Consumption
  • Present a Final Demo with Results and Insights
What can you do?
  • Build ML models using C#
  • Integrate ML into .NET apps
  • Use Model Builder and CLI tools
  • Deploy models to production
  • Automate training with AutoML
  • Create end-to-end ML pipelines in .NET
Frequently Asked Questions
No prior machine learning or data science experience is required. This course is designed for .NET developers and software engineers who want to learn machine learning using familiar tools like C# and Visual Studio. All core ML concepts are introduced from scratch, with practical examples guiding you through.
Absolutely. ML.NET enables you to build, train, and deploy models directly within .NET applications. By the end of the course, you’ll be able to integrate machine learning into desktop, web, or API-based applications, deploy them on Azure or other platforms, and even consume models via APIs.
ML.NET supports a wide range of tasks including regression (e.g., price prediction), classification (e.g., spam detection), clustering (e.g., customer segmentation), anomaly detection (e.g., fraud detection), recommendation systems, time series forecasting, and even image classification using ONNX and TensorFlow integration.