Introduction to Machine Learning from Scratch Playlist using NumPy and Python
This video introduces the new series I would be working on - Machine Learning from Scratch using NumPy and Python - Where I will break down every machine learning algorithm into three parts:
1- Intuition behind the Algorithm
2- Mathematical derivation and Understanding
3- Coding Algorithm from Scratch using NumPy and Comparison with similar sklearn class
I you are getting started with achine learning or are already experienced in it you will learn all the mathematics needed along with code implementation of every algorithm. This way of learning as you go would make your journey faster.
(00:00) Introduction
(00:40) Overview of the series
(01:16) Overview of linear regression and different flavors
(02:00) Starting with simple linear regression using least square method
(02:39) Moving on to multiple linear regression using least square method, Ridge, Lasso and Elastic Net regression
(03:15) Doing the same thing using gradient descent method
(03:47) Overview of Numpy and Jax library
(04:27) Overview of intuition, mathematical derivation, and coding from scratch using Numpy
(05:07) Comparing the custom class with Sklearn's built-in class
(05:46) Overview of future plans
(06:29) Overview of other machine learning algorithms
(07:02) Overview of plans for the series
(07:46) Importance of understanding each variable and hyperparameters
(08:25) Goal of the series
(08:57) Conclusion
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