Complete Machine Learning Engineering Tutorial | Supervised Learning Models | Hands-on Projects

Welcome to the world of Supervised Machine Learning! In this video, we’ll take you from beginner to machine learning pro with a step-by-step guide on 9 powerful ML models. Together, we'll explore real-world datasets, apply each model, and get hands-on with practical examples! You’ll learn how to build and train models like Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, K Nearest Neighbors, Gradient Boosting, Neural Networks, and Polynomial Regression.

Whether you’re starting from scratch or looking to sharpen your skills, this tutorial has everything you need to master the basics and start applying machine learning to solve real problems. Let's make machine learning fun, practical, and easy to understand!

🔑 What You’ll Learn:

The fundamentals of Supervised Machine Learning
How to train models using real-world datasets
When and how to use each of the 9 ML models
How to tune and optimize your models for better performance
No complicated jargon – just hands-on learning!
🎥 Timestamps:
0:00 - Introduction
1:00 - Linear Regression
3:00 - Logistic Regression
5:00 - Support Vector Machines
7:00 - Decision Trees
9:00 - Random Forest
11:00 - K Nearest Neighbors
13:00 - Gradient Boosting
15:00 - Neural Networks
17:00 - Polynomial Regression
19:00 - Conclusion

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[sameeppoudel]

Can you do similar type of tutorial for Deep learning including CNN, RNN, LSTM, GRU, GAN etc

[KingETHAN-]

Oh my, spent some time trying to find a practical tutorial and this is exactly what I wanted. Took me time to complete this, but have just completed. Most I found we just complete theory but I am a hands on person. Thanks again, I intend to use these projects in my interview next week. Wish me luck

[UgochiEze-vi]

please how did you get the data using or what did you do, I am a beginner and it is confusing

[AbdulwahabSabiu-dl]

Great! can we please have the lecture materials?