AI ML Step by Step Using C#, Python, Azure and OpenAI
Live Training + Recordings
What you'll learn :-
- This training is designed for C#, .NET Developers who are new to AI ML.
- It is 30Hours (2 Months) Training, will be conducted every Sturday and Sunday for 2 hour.
- Doubts Support related to the Training.
- Recordings of the live sessions
What are the pre-requisites ?
- Good Internet Connection
Training Content
AI ML Fundamentals :-
Introduction Road Map of AI ML for C# Developers.
- Lesson 1 (Theory) :- What is AI and ML?
- Lesson 2 (Theory) :- How do Humans Learn ? :- Features and Labels. Alphabet Image Data Format
- Lesson 3 (Theory) :- Features , Labels , Algo, Training , model :- FLATM
- Lesson 4 (Lab 1) :- Understanding FLATM using simple EXCEL.
- Lesson 5 (Theory) :- Algorithm ( Formula ) vs Model.
- Lesson 6 (Theory) :- Defining regression. Regression
- Lesson 7 (Lab 2) :- Simplest ML.NET Regression Code (Definition, MLContext ,& MKL components.)
- Lesson 8 (Lab 3):- Model is an Mathematical Formula.
- Lesson 9 (Theory) :- Inference VS Training
- Lesson 10 (Theory) Road Map for AI ML
- Lesson 11 (Theory) :- The psychology of ML.NET Code Pipeline.
- Lesson 12 (Lab 4) :- Multi-Features Example and Algorithm Confusion.
- Lesson 13 (Lab 5) :- OLS Ordinary Least Squares and SDCA Stochastic Dual Coordinate Ascent.
- Lesson 14(Lab 6) :- R Square and RMSE ( Root Mean Squared)
- Lesson 15 (Lab 7) :- AUTOML
- Lesson 16 Theory :- Everything is a VECTOR.
- Lesson 17 OLS with polynomial data
- Lesson 18 AutoML and Cosine and Euclidean
- Lesson 19 Feature Engineering.
- Lesson 20 Linear , Non-Linear and Seasonal.
- Lesson 21 Supervised Learning and Unsupervised Learning.
- Lesson 22 Clustering Algorithms and KMeans
- Lesson 23 What is MLP ?
- Lesson 24 Vector , Tokens , Encoding,Embedding , Transformer , BERT and GPT ?
- Lesson 25 MLP Encodings :- One-Hot Encoding , BOW , TF IDF , Word and Transformer Embeddings
- Lesson 26 Prompt Engineering ( Personal , Task , Context , Constraints and Format)
- Lesson 27 Data Quality (Descriptive Statistics, Outliers,,Min, Max, Median, Mode, Stdev, Skewness , Kurtosis, Quartiles )
Python Basic Lesson:-
- Lesson 28 :- Python basics comments , indents and blocks.
- Lesson 29 :- Variable declaration and Dynamism.
- Lesson 30 :- Simple For loops and Functions.
- Lesson 31 :- Arrays in Python.
- Lesson 32 :- Writing Classes , Functions and creating objects.
- Lesson 33 :- Packages , Modules , Classes and OOP
- Lesson 34 :- Numpy Fundamentals
- Lesson 35 :- Pandas Fundamentals.
Machine Learning Labs in C# and Python :-
- Lesson 36 :- AUTOML
- Lesson 37 :- Load Huge File and check AUTOML Suggestions and check Accuracy
- Lesson 38 :- Saving model and retraining through Live training ( SDCA and Online Gradient Descent)
- Lesson 39 :- Binary / Logistic regression.
- Lesson 40 :- Multi Class Classification
- Lesson 41 :- Simple Clustering Example using KMeans.
- Lesson 42 :- Understanding One Hot Encoding.
- Lesson 43 :- Simple Example of BOW
- Lesson 44 :- Simple example of TF-IDF
- Lesson 45 :- Example of WordEmbedding using GloVe50D and similarity checking using COSINE and Euclidean
- Lesson 46 :- Simple BERT Example.
- Lesson 47 :- GPT Example with Offline Encoding (Code does not work).
- Lesson 48 :- ChatGPT Demonstrating Transformer.
- Lesson 49 :- Simple RAG Demonstration
- Lesson 50 :- Chunking
1.Fixed Chunking ( overlap for connecting) :- LangChain.Splitters
2.Sentence based Chunking. LangChain.Splitters
3.Recursive based Chunking.( Paragraph → Line → Sentence → Word → Character)
4.Semantic based Chunking.
5.Hierarchical (Parent Child , Entity based , Lexical graph)
6.Topic based.7.Modality based.8.Agentic Chunking.
Neo4J
Connecting the Chunks
Overlap methodology ( Sliding window)
Parent Child
Contextual retrieval
Graph based
Meta Tagged - Lesson 51 :- Prompt Basics using ChatGpt
- Lesson 52 :- Pytorch Fundamentals
- Lesson 53 :- Creating a Model using Pytorch with simple Linear regression
- Lesson 54 :- Creating Model using multiple Layers
- Lesson 55 :- Consuming ONNX file.
- Lesson 56 :- Understanding using Tensor flow.
Azure AI :-
Basics of Azure.
Introduction to Azure AI Ecosystem
Basics of Azure.
Introduction to Azure AI Ecosystem
- Lesson 57 :- Creating Azure AI workspace, predicting simple linear regression using AUTOML
- Lesson 58 :- Creating Model using Azure AI Designer (Inference Pipeline).
- Lesson 59 :- Debugging AI issues in Azure.
- Lesson 60 :- Creating model using Azure AI Notebook.
- Lesson 61 :- Azure Foundry demo of Agents and Evals.
Setting Context :- MCP Model Context Protocol
Agent and Generative AI :-
- Lesson 62 :- Agentic AI with Semantic Kernel C#
- Lesson 63 :- Agentic AI with Langchain , graph Python
- Lesson 64 :- Simple N8N Demo with Manual trigger , Form trigger , Set fields , openAI and Webhooks.
- Lesson 65 :- Data Quality
- Lesson 66 :- MicroSoft Extension.AI
- Lesson 67 :- Generative AI
Projects :- Dotnet Interview Mate and Nifty Prediction
World-class training and development programs developed by top teachers
Whats Included
- World-class training teacher
- Bench has zero learning curve
- We handle the rest.