Gradient Descent from Scratch in Python Part 1 - Implement the Individual Steps

This is the 16th tutorial in the Python for Artificial Intelligence course series by Agni Data.
In this tutorial, we will implement all the individual steps involved in the Gradient Descent algorithm such as matrix multiplication, mean squared error loss calculation, gradient calculation and descent calculation from scratch in Python by just using the Numpy package.

This is an incredibly popular entry level data science interview question to test the candidate’s theoretical knowledge as well as Python coding skills. I hope you find it useful!

00:00 Introduction
00:20 Opening the Jupyter Notebook
00:48 Recap of Gradient Descent
01:45 Import the Numpy package
02:02 Matrix Multiplication using Numpy
04:52 Mean Squared Error Loss calculation from scratch in Numpy
08:21 Gradient (Partial Derivative) calculation from scratch in Numpy
11:26 Descent calculation in Numpy
13:56 Conclusion

If you like our content, please like and subscribe to support us! We highly appreciate your support.

Codes for this course:

I heavily use the Brave browser for my everyday work. You can download the Brave Browser using my referral code:

Python for Artificial Intelligence Full Free Course:

Demystifying Deep Learning Full Free Course:

For more information, visit us at

Social Media:
GitHub:
Twitter:
Facebook:
LinkedIn:

#python #ai #ml #dl #artificialintelligence #machinelearning #deeplearning #programming #datascience #dataanalytics #generalai #narrowai #agnidata

artificial intelligence,ai,machine learning,ml,python,data science,data analytics,general ai,narrow ai,deep learning,