.NET AI & ML Engineer Certification Training

Module 1: Machine Learning Basics for .NET Developers

What is Machine Learning?
ML vs traditional rule-based systems
Types of ML (Supervised, Unsupervised)
Common ML use cases in .NET applications
ML lifecycle overview
Introduction to ML.NET

Module 2: ML.NET Fundamentals & Architecture

ML.NET ecosystem and components
MLContext, IDataView, Data Loaders
ML tasks supported by ML.NET
End-to-end ML.NET workflow

Module 3: Data Loading & Preprocessing

Loading data from CSV and in-memory collections
Data schema and column mapping
Handling missing values
Feature engineering
Train-test data split

Module 4: ML.NET Pipelines & Transforms

Understanding ML pipelines
Chaining transforms
Normalization and encoding
Text featurization
Custom transforms

Module 5: Classification Models

Binary classification
Multiclass classification
Algorithms: SDCA, FastTree, LightGBM
Model evaluation metrics
Confusion matrix

Module 6: Regression & Forecasting

Regression fundamentals
Algorithms: SDCA, FastTree
Evaluation metrics (RMSE, R²)
Time-series forecasting basics

Module 7: Recommendation & Anomaly Detection

Recommendation systems overview
Matrix factorization
Anomaly detection use cases

Module 8: AutoML & Model Optimization

AutoML in ML.NET
Cross-validation
Hyperparameter tuning
Overfitting & underfitting

Module 9: Consuming ML Models in .NET Applications

Saving and loading ML.NET models
Using models in Console applications
Using models in ASP.NET Core Web API
PredictionEngine vs PredictionEnginePool
Performance considerations

Project: ML-powered ASP.NET Core API

Module 10: Deployment, Azure & Capstone Project

Deploying ML.NET applications
ML.NET with Azure App Service & Containers
Model monitoring and retraining strategies
Capstone: End-to-end ML.NET project

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Project#1: ML.NET Recommendation System

Building Intelligent Recommendation Engines

Introduction to Recommendation Systems
Understanding User Behavior and Interaction Data
Collaborative Filtering Concepts
Matrix Factorization using ML.NET
Training and Evaluating Recommendation Models
Generating Personalized Product Recommendations

Production-Ready Recommendation Services

Exposing Recommendations via ASP.NET Core Web API
Integrating Recommendation Engine with Applications
Handling Cold Start Problems
Optimizing Recommendation Accuracy and Performance
Scaling Recommendation Systems for Real Users
Amazon-Style Product Recommendation Use Cases

Project#2: ML.NET Fraud Detection System

AI-Powered Fraud Detection

Introduction to Fraud Detection in Financial Systems
Understanding Transaction Data and Fraud Patterns
Feature Engineering for Fraud Signals
Binary Classification using ML.NET
Anomaly Detection Techniques
Evaluating Fraud Detection Models (Precision, Recall)

Enterprise-Grade Fraud Prevention

Real-Time Fraud Detection with ASP.NET Core Web API
Reducing False Positives in Fraud Systems
Integrating Fraud Detection into Payment Workflows
Monitoring Model Performance Over Time
Retraining Models with New Transaction Data
Fraud Prevention for Banking, FinTech, and eCommerce

Q1. What is the .NET AI & ML Engineer Certification Training?

The .NET AI & ML Engineer Certification Training is a hands-on, job-ready program designed to help C# and .NET professionals build intelligent, data-driven applications using ML.NET. The course covers machine learning fundamentals, data preprocessing, model training, AutoML, and deployment of AI-powered features into ASP.NET Core and Azure-based applications.

Q3. What are the prerequisites for this training?

To enroll in this course, you should have:

Basic knowledge of C# and .NET
Experience with ASP.NET Core or Web APIs
Understanding of application architecture and data structures
No prior Machine Learning or Data Science experience is required

The course starts from ML fundamentals and gradually moves to advanced, real-world implementations.

Q5. What are the career benefits of this .NET AI & ML Engineer certification?

After completing this certification, you will be able to:

Add AI & ML engineering skills to your .NET developer profile
Build intelligent features like predictions, recommendations, and fraud detection
Work on AI-powered backend and API engineering roles
Move toward roles such as .NET AI Engineer, ML Engineer (.NET), or Solution Architect
Strengthen your career prospects in AI-driven enterprise and product teams

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.NET AI & ML Engineer Certification Training