Part 3 - Supervised Learning| Classification Algorithms for Beginners | Sheryians AI School

Instructor - Akarsh Vyas Welcome to Part 3 of our complete Machine Learning series. In this session, we dive into the world of Supervised Learning – Classification Models. From understanding what classification is to implementing multiple powerful algorithms, this video is packed with both theory and practical knowledge to help you build real-world classifiers. What you’ll learn: – What is Classification and where it’s used – Logistic Regression: The go-to for binary classification – K-Nearest Neighbors (KNN): Classifying by similarity – Decision Trees: Learning decisions step by step – Naive Bayes: Probabilistic and surprisingly powerful – Support Vector Machine (SVM): Drawing the best boundary – Evaluation Metrics to test your model: – Accuracy, Precision, Recall, F1-score – Confusion Matrix – reading and interpreting results – Hands-on Project: Titanic Survival Classification using real data Whether you're a beginner trying to understand classification or a student aiming to master multiple algorithms, this video blends concepts + code + clarity for maximum learning. Links: 📝 Suggestion – Create your own structured notes during the video 📚 My notes 🥲 – https://drive.google.com/file/d/1pQZ1... Titanic project Colab Notebook: https://colab.research.google.com/dri... Final project Github link - https://github.com/AkarshVyas/Machine... 📌 Don’t forget to check out Part 1 & Part 2 if you haven’t already. 👍 Like, share, and subscribe for more upcoming ML tutorials & hands-on projects! 00:00:00 - 00:01:07 Introduction 00:01:07 - 00:01:28 Important note 00:01:28 - 00:05:11 Structure of Video 00:05:11 - 00:08:32 What is Classification 00:08:32 - 00:27:27 Logistic Regression 00:27:27 - 00:31:21 Linear regression vs Logistic regression 00:31:21 - 00:45:11 Log Loss function 00:45:11 - 00:56:24 Logistic Regression Implementation 00:56:24 - 01:16:22 Model Evaluation 01:16:22 - 01:20:17 Model Evaluation Implementation 01:20:17 - 01:34:04 KNN 01:34:04 - 01:42:32 KNN Implementation 01:42:32 - 01:58:50 naive bayes 01:58:50 - 02:05:38 Naive bayes Implementation 02:05:38 - 02:34:36 Decision Trees 02:34:36 - 02:42:41 Decision Tree implementation 02:42:41 - 02:55:56 Basics of SVM 02:55:56 - 03:02:15 application of SVM 03:02:15 - 03:21:22 final project 03:21:22 - 03:39:04 frontend 03:39:04 - 03:39:37 outro