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AI/ML for C# Developers Training Recordings (ongoing)
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)
AI/ML Syllabus, Notes and GitHub Link
Fixing the model issues using Inference Pipeline. 22-01-2026
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