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.NET AI & ML Engineer Certification Training

.NET AI & ML Engineer Certification Training for .NET Developers is a hands-on, job-ready program to build intelligent applications using ML.NET. Learn machine learning fundamentals, data processing, model training, AutoML.
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12
live sessions
10
modules
45
lessons
Shailendra Chauhan
Your mentor Shailendra Chauhan Microsoft MVP, Founder & CEO at ScholarHat
12 live sessionsinstructor-led 10-module programstructured path Certificateon completion

.NET AI & ML Engineer Tools and Technologies Covered

C#
C#
.NET Core
ML.NET
Machine Learning
AI
NLP
Transformer
Web API
Web App
Azure SQL
Blob Storage
ChatGPT
Github Copilot
Interview Prep

.NET AI & ML Engineer Certification Training Key Features

6 Weeks of Intensive Live Training

Interactive sessions with real-time problem solving

Learn from Microsoft MVPs

Training by globally recognized experts

Build Azure AI based Projects

Hands-on project for your portfolio

Hands-On Labs

Practice real scenarios with guided, interactive labs

Interview Q&A

Frequently asked interview questions with clear answers

Quick Notes

Concise revision notes for fast and effective learning

.NET AI & ML Engineer Career Scope

ML Engineer
AI Engineer
.NET AI Architect
AI Architect
AI Lead
₹12 LPA
Avg package
35%
Avg hike
3,200+
Transitions
78%
Demand
Annual Salaries (₹)
₹15 LPA
Avg package
38%
Avg hike
3,200+
Transitions
75%
Demand
Annual Salaries (₹)
₹20 LPA
Avg package
40%
Avg hike
3,200+
Transitions
72%
Demand
Annual Salaries (₹)
₹24 LPA
Avg package
45%
Avg hike
3,200+
Transitions
75%
Demand
Annual Salaries (₹)
₹42 LPA
Avg package
80%
Avg hike
6,000+
Transitions
99%
Demand
Annual Salaries (₹)

Course Curriculum

  • 10 modules
  • 45 lessons
  • 12 live sessions
  1. Module 1 Machine Learning Basics for .NET Developers 6 lessons
    • 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
  2. Module 2 ML.NET Fundamentals & Architecture 4 lessons
    • ML.NET ecosystem and components
    • MLContext, IDataView, Data Loaders
    • ML tasks supported by ML.NET
    • End-to-end ML.NET workflow
  3. Module 3 Data Loading & Preprocessing 5 lessons
    • Loading data from CSV and in-memory collections
    • Data schema and column mapping
    • Handling missing values
    • Feature engineering
    • Train-test data split
  4. Module 4 ML.NET Pipelines & Transforms 5 lessons
    • Understanding ML pipelines
    • Chaining transforms
    • Normalization and encoding
    • Text featurization
    • Custom transforms
  5. Module 5 Classification Models 5 lessons
    • Binary classification
    • Multiclass classification
    • Algorithms: SDCA, FastTree, LightGBM
    • Model evaluation metrics
    • Confusion matrix
  6. Module 6 Regression & Forecasting 4 lessons
    • Regression fundamentals
    • Algorithms: SDCA, FastTree
    • Evaluation metrics (RMSE, R²)
    • Time-series forecasting basics
  7. Module 7 Recommendation & Anomaly Detection 3 lessons
    • Recommendation systems overview
    • Matrix factorization
    • Anomaly detection use cases
  8. Module 8 AutoML & Model Optimization 4 lessons
    • AutoML in ML.NET
    • Cross-validation
    • Hyperparameter tuning
    • Overfitting & underfitting
  9. Module 9 Consuming ML Models in .NET Applications 5 lessons
    • Saving and loading ML.NET models
    • Using models in Console applications
    • Using models in ASP.NET Core Web API
    • PredictionEngine vs PredictionEnginePool
    • Performance considerations
  10. Module 10 Deployment, Azure & Capstone Project 4 lessons
    • Deploying ML.NET applications
    • ML.NET with Azure App Service & Containers
    • Model monitoring and retraining strategies
    • Capstone: End-to-end ML.NET project
DOWNLOAD DETAILED CURRICULUM
  1. 01 Introduction to Artificial Intelligence 0:07:00
  2. 02 Introduction to Machine Learning 0:05:30
  3. 03 Introduction to Deep Learning 0:05:00
  4. 04 Data Ingestion in ML.NET 0:05:00
  5. 05 ML.NET Pipelines & Transform Chaining 0:06:00
  6. 06 Data Transformation in ML.NET 0:04:00
  7. 07 Core ML.NET and Binary Classification 0:05:00
  8. 08 TorchSharp Fundamentals: Neural Networks and Tensors 0:04:00
  9. 09 TorchSharp Training Pipeline, Autograd, and Dataset Management 0:03:00
  10. 10 Model Evaluation Metrics & Confusion Matrix 0:07:00
  11. 11 Regression in ML.NET 0:05:00
  12. 12 Recommendation Systems & Matrix Factorization 0:06:00
  13. 13 AutoML in ML.NET 0:07:00
  1. 01 Introduction to Machine Learning with ML.NET 00:30:00
  2. 02 Rule-Based Classification vs Machine Learning Models 00:30:00
  3. 03 Importing CSV Data into ML.NET (TextLoader) 00:30:00
  4. 04 ML.NET In-Memory Data Loading, Renaming, and Categorical Encoding 01:00:00
  5. 05 Handling Missing Values in ML.NET 01:00:00
  6. 06 Basic Feature Engineering + Train-Test Split with ML.NET 01:00:00
  7. 07 ML.NET Pipeline for Data Normalization with MinMax Scaler 01:00:00
  8. 08 Using TextFeaturizingEstimator in ML.NET for Bag-of-Words Feature Extraction 01:00:00
  9. 09 Building an ML.NET Data Processing Pipeline 00:30:00
  10. 10 Binary Classification for Sentiment Analysis Using ML.NET 01:00:00
  11. 11 Confusion Matrix & Metrics Deep Dive with ML.NET 01:00:00
  12. 12 Comparing FastTree vs LightGBM on Iris Dataset 01:00:00
  13. 13 First Regression – Taxi Fare Price Prediction (SDCA Regressor) 01:00:00
  14. 14 Understanding Regression Metrics: RMSE, R², and MAE 01:00:00
  15. 15 Time-Series – Moving Average Baseline vs Lag Regression 01:00:00
  16. 16 Movie Recommendation System using Matrix Factorization 01:00:00
  17. 17 Anomaly Detection – Spike Detection with ML.NET (SrCnn Version) 01:00:00
  18. 18 Building a Simple Sentiment Analysis Model with ML.NET (Binary Classification) 01:00:00
  19. 19 Cross-Validation & Overfitting Check in ML.NET (Multiclass Classification - Iris Dataset) 01:00:00
  20. 20 Save Trained Model & Make Predictions in a Separate Console Application 01:00:00
  21. 21 Predicting Student Pass/Fail Using ML.NET Binary Classification 01:00:00
  1. 01 Deep Learning with TorchSharp 15 questions
  1. Q&A guideAI & Machine Learning Foundations 2 questions
    • Machine Learning vs Traditional Rule-Based Systems 0:18:00
    • Types of Machine Learning (Supervised, Unsupervised, Semi-Supervised) 0:15:00
  2. Q&A guideDeep Learning with TorchSharp 5 questions
    • Introduction to Deep Learning & TorchSharp 0:16:00
    • Neural Network Fundamentals & Tensors 0:18:00
    • Building & Training Neural Networks with TorchSharp 0:20:00
    • Autograd & Data Flow through Neural Networks 0:15:00
    • TorchSharp Use Cases & ML Datasets 0:17:00
  3. Q&A guideData Engineering & Feature Preparation 3 questions
    • Data Preprocessing and Handling Missing Values 0:20:00
    • Feature Engineering and Feature Selection 0:16:00
    • Train-Test Split and Data Preparation Strategies 0:17:00
  4. Q&A guideML Pipelines & Data Transformation 5 questions
    • End-to-End ML.NET Workflow 0:19:00
    • Data Schema, Column Mapping, and Validation 0:15:00
    • ML.NET Pipelines and Transformers 0:20:00
    • Data Normalization, Encoding, and Custom Transforms 0:17:00
    • Text Feature Extraction and NLP Basics in ML.NET 0:15:00
  5. Q&A guideClassification Models & Evaluation 2 questions
    • Classification Models in ML.NET 0:19:00
    • Model Evaluation Metrics (Accuracy, Confusion Matrix, Precision, Recall) 0:16:00
  6. Q&A guideRegression, Forecasting & Recommendation Systems 4 questions
    • Regression Models and Forecasting Techniques 0:18:00
    • Model Evaluation Metrics (RMSE, R², MAE) 0:16:00
    • Recommendation Systems and Matrix Factorization 0:20:00
    • Anomaly Detection Techniques 0:16:00
  7. Q&A guideAutoML & Model Optimization 4 questions
    • Automated Machine Learning with ML.NET 0:20:00
    • Model Validation with Cross-Validation 0:17:00
    • Hyperparameter Tuning and Optimization 0:15:00
    • Understanding Overfitting and Underfitting 0:19:00
  8. Q&A guideModel Consumption, Deployment & Capstone 1 questions
    • AI/ML Integration Patterns in .NET Applications 0:19:00

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.NET AI & ML Engineer Certification

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.

This program is ideal for:

  • .NET Developers (C#, ASP.NET Core)
  • Full-Stack .NET Engineers
  • Backend Developers building intelligent APIs
  • Software & Solution Architects
  • Developers looking to transition into AI/ML Engineer roles using .NET

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.

  • ML.NET
  • C# and .NET 8/9
  • ASP.NET Core Web API
  • ML.NET AutoML
  • Data preprocessing & feature engineering
  • Model evaluation and optimization
  • Azure App Service & Container-based deployments (overview)

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
Verifiable Credential .NET AI & ML Engineer Certification certificate

Course Mentors

Shailendra Chauhan
11X MICROSOFT MVP AI ARCHITECT

Shailendra Chauhan

Microsoft MVP, Founder & CEO at ScholarHat

17+ Years of Industry Experience as Mentor & Solution Architect
Expert in .NET, Angular, React & Python
Azure Cloud & AI/ML/Gen AI Specialist
Bhawna Gunwani

Bhawna Gunwani

Corporate Trainer

15+ Years Technical & Corporate Training
Expert in Microsoft Tech, React, Angular & Node
Global Training: TCS, Infosys, Accenture & More
Rahul Kumar

Rahul Kumar

Author and .NET Tech Lead

16+ Years of Industry Experience as .NET Mentor & Tech Lead
Mastery in .NET Development Practices
Lifelong Innovator & Team Inspirer
Shailendra Chauhan
11X MICROSOFT MVP AI ARCHITECT

Shailendra Chauhan

Microsoft MVP, Founder & CEO at ScholarHat

17+ Years of Industry Experience as Mentor & Solution Architect
Expert in .NET, Angular, React & Python
Azure Cloud & AI/ML/Gen AI Specialist
View Profile
Bhawna Gunwani

Bhawna Gunwani

Corporate Trainer

15+ Years Technical & Corporate Training
Expert in Microsoft Tech, React, Angular & Node
Global Training: TCS, Infosys, Accenture & More
View Profile
Rahul Kumar

Rahul Kumar

Author and .NET Tech Lead

16+ Years of Industry Experience as .NET Mentor & Tech Lead
Mastery in .NET Development Practices
Lifelong Innovator & Team Inspirer
View Profile

Frequently Asked Questions

Q1. What is the Azure AI Cloud Sandbox?
The Azure AI Cloud Sandbox is a secure, isolated environment that lets you practice and experiment with Azure AI services without affecting production resources. It provides hands-on access to real Azure AI capabilities in a risk-free setup.
Q2. Can I Attend a Demo Session before Enrolment?
Yes, you can Attend a Demo Session before Enrolment in angular certification course. It gives you the opportunity to assess whether the training program aligns with your learning objectives. So, don't hesitate! Take advantage of this opportunity and attend a demo session before making your decision.
Q3. Can I request for a support session if I need to better understand the topics?
Yes, of course you can request for a support session if you need to better understand the topics. For that, you need to be in touch with the counsellor. Contact on +91- 999 9123 502 or you can mail us at hello@scholarhat.com
Q4. Who are your mentors?
All our mentors are highly qualified and experience professionals. All have at least 8-10 yrs of development experience in various technologies and are trained by ScholarHat to deliver interactive training to the participants.
Q5. What If I miss my online training class?
All online training classes are recorded. You will get the recorded sessions so that you can watch the online classes when you want. Also, you can join other class to do your missing classes.
Q6. Can I share my course with someone else?
In short, no. Check our licensing that you agree to by using ScholarHat LMS. We track this stuff, any abuse of copyright is taken seriously. Thanks for your understanding on this one.
Q7. Do you provide any course material or live session videos?
Yes we do. You will get access to the entire content including class videos, mockups, and assignments through LMS.
Q8. Do you provide training on latest technology version?
Yes we do. As the technology upgrades we do update our content and provide your training on latest version of that technology.
Q9. Do you prepare me for the job interview?
Yes, we do. We will discuss all possible technical interview questions and answers during the training program so that you can prepare yourself for interview.
Q10. Will I get placement assistance after receiving my course completion certificate?
Yes, you’ll get placement assistance after receiving your course completion certificate. The placement assistance provided by the US will guide you through the job search process, help you polish your resume, and connect you with potential employers. For that, you need to be in touch with the counsellor. Contact on +91- 999 9123 502 or you can mail us at hello@scholarhat.com
Still have some questions? Let's discuss.
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