AI ML Step by Step Using C#, Python, Azure and OpenAI :-

Python Basics :-

• Getting your first Python Program Running.
• Python Data types, strings, statements and functions.
• Object Oriented Programming and Error handling 
• Modules and Packages
• Numpy and Pandas

AI, ML Fundamentals :-

• What is AI and ML?
• Different ways to implement AI:- Rule based, ML , DL, probability, Fuzzy logic
• Simple example of ML using Python and C#.
• C# ML.NET Introduction.
• MLContext and MLData.
• Model, features and Labels
• Understanding the Model, features and Labels using simple Linear Regression.
• Model and mathematical formula connection
• Linear vs Polynomial vs Exponential: - Different data different mode
• Simple example if Linear , Polynomial and Exponential using C# and Python
• Supervised, Unsupervised and reinforcement learning.
• Supervised: - Regression and Classification.
• Unsupervised: - Clustering and Dimensionality reduction
• Q Learning and Deep Q networks.
• LLM’s and LLM’s :- GPT, Claude , LLAMA ,Mistral
• Setting Context :- MCP Model Context Protocol
• Agent and Generative AI
• Orchestration using N8N.
• GitHub for AI Models: - Hugging Face.

Prompt Engineering :-

• Prompt Engineering (Personal, Task, Context, Constraints and Format)
• JSONL files: - Persona, System, Rules and Settings.

Azure AI :-

• Identifying Machine Learning Types
• AutoML :- No Code machine learning.
• Computer vision work loads.
• Computer vision work loads.
• Generative AI work loads
• Azure Open AI service