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/1pQZ1Zga_u4z2L1ogLxU41Y_7LI1TLaPY/view?usp=sharing
Titanic project
Colab Notebook: https://colab.research.google.com/drive/1iIujBR7WcySa15Z3JLdVRKcHT7YO-q9X?usp=sharing
Final project Github link – https://github.com/AkarshVyas/Machine-Learning-Part-3
📌 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
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