Instructor - Akarsh Vyas Welcome to the first step of your Machine Learning journey! In this video, we’ll walk through the complete foundation of a real-world ML project, covering everything you must know before building any model. you can download the CSV files and code from here. Code link - https://github.com/AkarshVyas/Machine-Learning-Part-1 All the notes of our classes are here Notes - https://drive.google.com/file/d/16GDJ6Ut9IX0RNYDnGUynbrjeftfjnB59/view?usp=sharing Here's what you'll learn: How to define the problem clearly Where and how to collect quality data How to perform Exploratory Data Analysis (EDA) Techniques for data cleaning and preprocessing Feature selection to choose the right data Feature engineering to boost model performance These are the most critical and often ignored steps in ML — but they make or break your model’s success. Whether you're a beginner or refreshing your knowledge, this video sets the stage for smarter models and real-world success. Start here. Build right. 00:00 - 00:35 - Introduction 00:35 - 02:54 - Content 02:54 - 06:52 - what is machine learning 06:52 - 08:47 - Real life machine learning applications 08:47 - 10:22 - Traditional programming vs machine learning 10:22 - 14:34 - Difference b/w AI,ML,DL 14:34 - 25:20 - Types of Machine Learning 25:20 - 28:15 - Steps for making a machine learning model 28:15 - 33:45 - EDA 33:45 - 42:30 - DATA cleaning 42:30 - 54:10- DATA Preprocessing 54:10 - 57:58 - Feature Engineering 57:58 - 01:01:42 - Feature Selection 01:01:42 - 02:07:15 - Project 1 02:07:15 - 02:42:43- Project 2 02:42:43 - 02:43:07 -outro