02 All about Machine Learning (ML) | Basics of Machine Learning | Classification & Regression Models

Generative AI | LLM | GenAI | ML | Classification | Regression ⏰ Scheduled to be Public from Members Only on 06th May 2026 18:45 HRS IST ⏰ ===== In this video, you will learn ===== Fundamental of Machine Learning (ML). What are Features and Labels? What are Classification Models? What are Regression Models? What is Curse of Dimension? How Logistic Regression works? What are Feature Engineering techniques? How to determing ML Model Accuracy? ===== Chapters ===== 00:00 - Introduction 00:33 - What is Machine Learning? 00:58 - What are Features and Labels? 03:43 - Machine Learning Workflow (ML Workflow) 07:28 - Training and Testing Data 09:39 - Supervised Learning 10:09 - Classification vs Regression Model 12:20 - Logistic Regression (Classification Model) 17:55 - Decision Tree (Classification Model) 19:23 - Random Forest (Classification Model) 21:28 - Confusion Matrix 24:56 - Linear & Polynomial Regression Models 28:02 - Accuracy for Linear Regression Models 29:29 - Unsupervised Learning 31:40 - Reinforcement Learning 33:31 - Curse of Dimensions 37:12 - Feature Engineering in ML 37:49 - Rescaling (Normalization/Standardization) 39:02 - One Hot Encoding 40:30 - Handling Missing Data 41:14 - Feature Extraction 42:42 - What's next? ===== Other Playlists ===== Checkout all other playlists on Data Engineering 👇🏻 https://www.youtube.com/@easewithdata/playlists ===== GitHub Repo ===== https://github.com/subhamkharwal ===== Connect with ME ===== LinkedIn - https://www.linkedin.com/in/subhamkharwal Medium - https://subhamkharwal.medium.com ===== Hashtags ==== #genai #dataengineering #python #langchain #langgraph #agenticai #aiagents #aiagent #ml