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AI/ML for C# Developers Training Recordings
AI/ML using C# Training Recordings
Day 1: Introduction to C# AI/ML -08-11-2025. (61:26)
Day 2: Compare OLS, SDCA and OLS Square using R Square & RMSE. -09-11-2025 (61:19)
Day 3: Understanding Feature Engineering, Online Algorithm Performance and key factors in Linear, Seasonal and Non-linear Predictions. -15-11-2025 (64:43)
Day 4: Machine Learning Algorithms: Classification, Clustering & NLP. -16-11-2025 (57:48)
Day 5: Understanding NLP (Natural Language Processing) methodologies. 22-11-2025 (55:22)
Day 6: Using Embeddings and Transformers to understand Relationship Context. 23-11-2025 (49:13)
Day 7: Introduction to GPT, BERT and ChatGPT using Prompts. 29-11-2025 (68:46)
Day 8: AI/ML Training Revision. 30-11-2025 (64:47)
Day 9: Getting started with Python Basics. 06-12-2025 (43:18)
Day 10: Python Coding - Create classes, OOP Programming and Creating packages. 07-12-2025 (72:38)
Day 11: Linear Regression - a simple Single-Prediction example in ML.NET and Python. 13-12-2025 (54:48)
Day 12: Linear Regression in ML.NET/Python using Test Data. 14-12-2025 (58:47)
Day 13: Pytorch and Tensor Introduction. 21-12-2025 (36:39)
Day 14:- Revision and Creating Model using Pytorch. 03-01--2026 (50:58)
Day 15:- Creating Azure AI Workspace , Executing Automated ML with simple linear regression and testing the output. 04-01-2026 (60:08)
Day 16:- Creating a full model with Multiple Layers using Pytorch. 10-01-2026 (51:18)
Day 17:- Creating /Publishing/Consuming a simple AI Model of a flight delay data using Azure AI Designer with Logistic Regression. 17-01-2026 (56:46)
Day 18:- Debugging Azure AI. 18-01-2026 (48:52)
Fixing the model issues using Inference Pipeline. 22-01-2026 (5:28)
Day 19:- Tensor Flow implementation. 01-02-2026 (63:07)
Day 20:- Azure Notebooks and Revision. 07-02-2026 (55:30)
Day 21:- Agentic AI using Semantic Kernel. 08-02-2026 (54:10)
Day 22:- Agentic AI using Langchain/Langgraph in Python. 14-02-2026 (32:25)
Day 23:- N8N Demo with OpenAI, Webhooks and Forms. 15-02-2026 (24:47)
Day 24:- Data Quality (Outliers, Min, Max, Median, Mode, Stde , Skewness, Kurtosis, Quartiles ). 22-02-2026 (65:50)
Day 25:- Revisiting RAG and implementing the same with SQL Server. 28-02-2026 (36:37)
Day 26:- Integrating AllMini embedding with OpenAI and SQL Server. 01-03-2026 (34:28)
Day 27:- Rag, LLM, Interaction, loose coupling. 07-03-2026 (45:15)
Day 28:- Model Context Protocol (MCP). 14-03-2026 (29:19)
Day 29:- Create your own MCP host. 15-03-2026 (29:24)
Day 30:- Creating MCP Client with OpenAI (More in detail). 21-03-2026 (29:42)
Day 31:- Guard Rails. 28-03-2026 (55:24)
AI/ML Syllabus, Notes and GitHub Link
Day 24:- Data Quality (Outliers, Min, Max, Median, Mode, Stde , Skewness, Kurtosis, Quartiles ). 22-02-2026
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