{"product_id":"ai-ml-step-by-step-using-c-python-azure-and-openai-1","title":"AI ML Step by Step Using C#, Python, Azure and OpenAI","description":"\u003ch3\u003e\u003cstrong\u003eAI ML Fundamentals :-\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cdiv\u003e\n\u003cstrong\u003eIntroduction Road Map of AI ML for C# Developers.\u003cbr\u003e\u003c\/strong\u003e\n\u003cdiv\u003e\n\u003cul\u003e\n\u003cli\u003eLesson 1 (Theory) :- What is AI and ML?\u003c\/li\u003e\n\u003cli\u003eLesson 2 (Theory) :- How do Humans Learn ? :- Features and Labels. Alphabet Image Data Format\u003c\/li\u003e\n\u003cli\u003eLesson 3 (Theory) :- Features , Labels , Algo, Training , model :- FLATM\u003c\/li\u003e\n\u003cli\u003eLesson 4 (Lab 1) :- Understanding FLATM using simple EXCEL.\u003c\/li\u003e\n\u003cli\u003eLesson 5 (Theory) :- Algorithm ( Formula ) vs Model.\u003c\/li\u003e\n\u003cli\u003eLesson 6 (Theory) :- Defining regression. Regression\u003c\/li\u003e\n\u003cli\u003eLesson 7 (Lab 2) :- Simplest ML.NET Regression Code (Definition, MLContext ,\u0026amp; MKL components.)\u003c\/li\u003e\n\u003cli\u003eLesson 8 (Lab 3):- Model is an Mathematical Formula.\u003c\/li\u003e\n\u003cli\u003eLesson 9 (Theory) :- Inference VS Training\u003c\/li\u003e\n\u003cli\u003eLesson 10 (Theory) Road Map for AI ML\u003c\/li\u003e\n\u003cli\u003eLesson 11 (Theory) :- The psychology of ML.NET Code Pipeline.\u003c\/li\u003e\n\u003cli\u003eLesson 12 (Lab 4) :- Multi-Features Example and Algorithm Confusion.\u003c\/li\u003e\n\u003cli\u003eLesson 13 (Lab 5) :- OLS Ordinary Least Squares and SDCA Stochastic Dual Coordinate Ascent.\u003c\/li\u003e\n\u003cli\u003eLesson 14(Lab 6) :- R Square and RMSE ( Root Mean Squared)\u003c\/li\u003e\n\u003cli\u003eLesson 15 (Lab 7) :- AUTOML\u003c\/li\u003e\n\u003cli\u003eLesson 16 Theory :- Everything is a VECTOR.\u003c\/li\u003e\n\u003cli\u003eLesson 17 OLS with polynomial data\u003c\/li\u003e\n\u003cli\u003eLesson 18 AutoML and Cosine and Euclidean\u003c\/li\u003e\n\u003cli\u003eLesson 19 Feature Engineering.\u003c\/li\u003e\n\u003cli\u003eLesson 20 Linear , Non-Linear and Seasonal.\u003c\/li\u003e\n\u003cli\u003eLesson 21 Supervised Learning and Unsupervised Learning.\u003c\/li\u003e\n\u003cli\u003eLesson 22 Clustering Algorithms and KMeans\u003c\/li\u003e\n\u003cli\u003eLesson 23 What is MLP ?\u003c\/li\u003e\n\u003cli\u003eLesson 24 Vector , Tokens , Encoding,Embedding , Transformer , BERT and GPT ?\u003c\/li\u003e\n\u003cli\u003eLesson 25 MLP Encodings :- One-Hot Encoding , BOW , TF IDF , Word and Transformer Embeddings\u003c\/li\u003e\n\u003cli\u003eLesson 26 Prompt Engineering ( Personal , Task , Context , Constraints and Format)\u003c\/li\u003e\n\u003cli\u003eLesson 27 Data Quality (Descriptive Statistics, Outliers,,Min, Max, Median, Mode, Stdev, Skewness , Kurtosis, Quartiles )\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cdiv\u003e\u003cstrong\u003ePython Basic Lesson:-\u003c\/strong\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cul\u003e\n\u003cli\u003eLesson 28 :- Python basics comments , indents and blocks.\u003c\/li\u003e\n\u003cli\u003eLesson 29 :- Variable declaration and Dynamism.\u003c\/li\u003e\n\u003cli\u003eLesson 30 :- Simple For loops and Functions.\u003c\/li\u003e\n\u003cli\u003eLesson 31 :- Arrays in Python.\u003c\/li\u003e\n\u003cli\u003eLesson 32 :- Writing Classes , Functions and creating objects.\u003c\/li\u003e\n\u003cli\u003eLesson 33 :- Packages , Modules , Classes and OOP\u003c\/li\u003e\n\u003cli\u003eLesson 34 :- Numpy Fundamentals\u003c\/li\u003e\n\u003cli\u003eLesson 35 :- Pandas Fundamentals.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cdiv\u003e\u003cstrong\u003eMachine Learning Labs in C# and Python\u003cspan\u003e \u003c\/span\u003e:-\u003c\/strong\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cul\u003e\n\u003cli\u003eLesson 36 :- AUTOML\u003c\/li\u003e\n\u003cli\u003eLesson 37 :- Load Huge File and check AUTOML Suggestions and check Accuracy\u003c\/li\u003e\n\u003cli\u003eLesson 38 :- Saving model and retraining through Live training ( SDCA and Online Gradient Descent)\u003c\/li\u003e\n\u003cli\u003eLesson 39 :- Binary \/ Logistic regression.\u003c\/li\u003e\n\u003cli\u003eLesson 40 :- Multi Class Classification\u003c\/li\u003e\n\u003cli\u003eLesson 41 :- Simple Clustering Example using KMeans.\u003c\/li\u003e\n\u003cli\u003eLesson 42 :- Understanding One Hot Encoding.\u003c\/li\u003e\n\u003cli\u003eLesson 43 :- Simple Example of BOW\u003c\/li\u003e\n\u003cli\u003eLesson 44 :- Simple example of TF-IDF\u003c\/li\u003e\n\u003cli\u003eLesson 45 :- Example of WordEmbedding using GloVe50D and similarity checking using COSINE and Euclidean\u003c\/li\u003e\n\u003cli\u003eLesson 46 :- Simple BERT Example.\u003c\/li\u003e\n\u003cli\u003eLesson 47 :- GPT Example with Offline Encoding (Code does not work).\u003c\/li\u003e\n\u003cli\u003eLesson 48 :- ChatGPT Demonstrating Transformer.\u003c\/li\u003e\n\u003cli\u003eLesson 49 :- Simple RAG Demonstration\u003c\/li\u003e\n\u003cli\u003eLesson 50 :- Chunking\u003cbr\u003e  1.Fixed Chunking ( overlap for connecting) :- LangChain.Splitters\u003cbr\u003e  2.Sentence based Chunking. LangChain.Splitters\u003cbr\u003e  3.Recursive based Chunking.( Paragraph → Line → Sentence → Word → Character)\u003cbr\u003e  4.Semantic based Chunking.\u003cbr\u003e  5.Hierarchical (Parent Child , Entity based , Lexical graph)\u003cbr\u003e  6.Topic based.7.Modality based.8.Agentic Chunking.\u003cbr\u003eNeo4J\u003cbr\u003eConnecting the Chunks\u003cbr\u003eOverlap methodology ( Sliding window)\u003cbr\u003eParent Child\u003cbr\u003eContextual retrieval\u003cbr\u003eGraph based\u003cbr\u003eMeta Tagged\u003c\/li\u003e\n\u003cli\u003eLesson 51 :- Prompt Basics using ChatGpt\u003c\/li\u003e\n\u003cli\u003eLesson 52 :- Pytorch Fundamentals\u003c\/li\u003e\n\u003cli\u003eLesson 53 :- Creating a Model using Pytorch with simple Linear regression\u003c\/li\u003e\n\u003cli\u003eLesson 54 :- Creating Model using multiple Layers\u003c\/li\u003e\n\u003cli\u003eLesson 55 :- Consuming ONNX file.\u003c\/li\u003e\n\u003cli\u003eLesson 56 :- Understanding using Tensor flow.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cdiv\u003e\u003cstrong\u003eAzure AI :-\u003cbr\u003e\u003cbr\u003eBasics of Azure.\u003cbr\u003eIntroduction to Azure AI Ecosystem\u003c\/strong\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cul\u003e\n\u003cli\u003eLesson 57 :- Creating Azure AI workspace, predicting simple linear regression using AUTOML\u003c\/li\u003e\n\u003cli\u003eLesson 58 :- Creating Model using Azure AI Designer (Inference Pipeline).\u003c\/li\u003e\n\u003cli\u003eLesson 59 :- Debugging AI issues in Azure.\u003c\/li\u003e\n\u003cli\u003eLesson 60 :- Creating model using Azure AI Notebook.\u003c\/li\u003e\n\u003cli\u003eLesson 61 :- Azure Foundry demo of Agents and Evals.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cdiv\u003eSetting Context :- MCP Model Context Protocol\u003cbr\u003e\u003cbr\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003cstrong\u003eAgent and Generative AI :-\u003c\/strong\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cul\u003e\n\u003cli\u003eLesson 62 :- Agentic AI with Semantic Kernel C#\u003c\/li\u003e\n\u003cli\u003eLesson 63 :- Agentic AI with Langchain , graph Python\u003c\/li\u003e\n\u003cli\u003eLesson 64 :- Simple N8N Demo with Manual trigger , Form trigger , Set fields , openAI and Webhooks.\u003c\/li\u003e\n\u003cli\u003eLesson 65 :- Data Quality\u003c\/li\u003e\n\u003cli\u003eLesson 66 :- MicroSoft Extension.AI\u003c\/li\u003e\n\u003cli\u003eLesson 67 :- Generative AI\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cdiv\u003e\u003cstrong\u003eProjects :- Dotnet Interview Mate and Nifty Prediction\u003c\/strong\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Questpond Videos","offers":[{"title":"Default Title","offer_id":48332737708238,"sku":null,"price":20000.0,"currency_code":"INR","in_stock":true}],"url":"https:\/\/questpondshop.myshopify.com\/products\/ai-ml-step-by-step-using-c-python-azure-and-openai-1","provider":"Questpond Videos","version":"1.0","type":"link"}