Linear Regression, Gradient Descent, Logistic Regression, NumPy, Pandas | Machine Learning Tutorial

Welcome to the second class on Machine Learning.
In this video, we will go through the math and code of popular ML algorithms used in Supervised Learning, like Linear Regression, Logistic Regression. And also understand how Gradient Descent works in optimizing a cost function with math explanation and in Python.

Who is this for?

Pre-requisites:
- Math (Linear Algebra, Calculus, Probability)
- Python (basic syntax and loops)

Why Watch?

To understand the intuition behind how Machine Learning algorithms predict using data.

~~~~~~~ Timestamps ~~~~~~~


0:00 - Introduction to ML and Supervised Learning
10:30 - Deriving slope and equation of a line
17:37 - Linear Regression
25:05 - Cost Function & Gradient Descent
44:02 - NumPy
50:30 - Pandas
1:04:19 - Gradient Descent in Python
1:12:52 - Linear Regression in Python
1:20:50 - Housing Price Prediction Jupyter Notebook
1:27:50 - Google Colab & Anaconda Distribution
1:30:56 - Summary of Linear Regression
1:37:29 - Logistic Regression
1:41:30 - Deriving the logistic function & sigmoid activation function
1:52:01 - Conclusion

~~~~~~~ End ~~~~~~~

Link to the code: https://github.com/souvikr/ai/
All material on my website: https://azure-liquid-d23.notion.site/...


🔔 Don’t forget to subscribe to stay updated on part 2.

In Part 2, we will discuss:
- Decision Trees
- Random Forest
- Gradient Boosting
- Naive Bayes
- K-Nearest Neighbor
- Support Vector Machine (SVM)

📚 Additional Resources:
https://mlcourse.ai/

📱 Connect with me:
Instagram: / souvikroy5
LinkedIn: / souvikroy5