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
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