🐍 Guide to Mastering Linear Regression In Python: Math, Code, Matrices, Vectors, R2, RSS, TSS

Learn how Linear Regression Model works and the Mathematics behind it using Python programming language. We’ll discuss Linear Algebra of Ordinary Least Squares Regression (OLS), Evaluation Metrics (Coefficient of Determination & Mean Squared Error) & some general Machine Learning concepts.

This video is part of a series covering analytics and machine learning in Python where you will learn by doing full projects.

Prerequisites:

Chapters:
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00:00 Function Approximation & Multiple Linear Regression Equation
01:15 Linear Algebra refresher (matrices, vectors, norms)
06:24 Represent Data in Matrix & Vector form
07:51 Dot Product
10:34 Identity Matrix, Matrix inverse & Square Matrix
13:25 Normal Equation (Estimate Regression Coefficients)
18:35 Build Linear Regression Model from Scratch
22:10 Singular Matrix
28:30 Congrats! You Earned a Bonus
29:06 Categorical Data Transformation (One Hot Encoding) & Multicollinearity
36:26 Test Model Generalizability (Split your data into training and testing sets)
38:19 Coefficient of Determination R2, Residual Sum of Squares (RSS) & Total Sum of Squares (TSS)
42:03 Overfitting & Cross Validation
45:09 What exactly is Coefficient of Determination R2?
47:50 Mean Squared Error, Root Mean Squared Error, Variance & Standard Deviation
50:54 Linear Regression for Single Variable
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Let’s Connect:

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Notes:

Subtitles available for this video
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Soundtrack – Intro:

Track: Max Brhon - Redemption [NCS Release]
Music provided by NoCopyrightSounds.

Track: Egzod, Maestro Chives, Neoni - Royalty [NCS Release]
Music provided by NoCopyrightSounds.
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Soundtrack – Outro:

Track: Yonexx & lunar - Need You [NCS Release]
Music provided by NoCopyrightSounds.

Track: Rex Hooligan x Anna Simone - No More Levitation [NCS Release]
Music provided by NoCopyrightSounds.
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#linearalgebra #linearregression #machinelearning #datascience #dataanalytics #pythonprogramming

[henrikschulze]

Excellent video was immensely helpful!!!