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Machine Learning with Python! Simple Linear Regression
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Tutorial on how to code a simple linear regression. Learn about linear functions, different methods to code out a simple linear regression without a package, testing the assumptions of the model and test how well the model predicts a data process.
**Please note when comparing the linear regression and KNN regression, the data sets should have been split into training and testing data sets.**
CONNECT:
|-Video Chapters-|
0:00 - Intro
0:28 - Linear functions
4:13 - Creating an artificial data set
12:08 - Assumptions of Linear Regression
13:22 - Simple Linear Regression model by algebra
25:07 - Simple Linear Regression model by linear algebra
34:08 - Comparing the hand coded models to sklearn
36:55 - Measuring the model with mean squared error
42:58 - Assumption 1: Linear relationship between the dependent and independent variables
45:23 - Assumption 2: Independence of errors
49:15 - Assumption 3: Normality of errors
50:24 - Assumption 4: Homoscedasticity
52:00 - References and additional learning
**Please note when comparing the linear regression and KNN regression, the data sets should have been split into training and testing data sets.**
CONNECT:
|-Video Chapters-|
0:00 - Intro
0:28 - Linear functions
4:13 - Creating an artificial data set
12:08 - Assumptions of Linear Regression
13:22 - Simple Linear Regression model by algebra
25:07 - Simple Linear Regression model by linear algebra
34:08 - Comparing the hand coded models to sklearn
36:55 - Measuring the model with mean squared error
42:58 - Assumption 1: Linear relationship between the dependent and independent variables
45:23 - Assumption 2: Independence of errors
49:15 - Assumption 3: Normality of errors
50:24 - Assumption 4: Homoscedasticity
52:00 - References and additional learning
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