Machine Learning
Machine Learning in R and Python
Embedded Files
Part 2 - Regression
Section 04 - Simple Linear Regression
Simple Linear Regression Intuition
Simple Linear Regression in Python
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Simple Linear Regression to the Training set
Predicting the Test set results
Visualising the Training set results
Visualising the Test set results
Simple Linear Regression in Python - Backward Elimination
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Simple Linear Regression to the Training set
Predicting the Test set results
Visualising the Training set results
Visualising the Test set results
Simple Linear Regression in R - Backward Elimination
Section 05 - Multiple Linear Regression
Multiple Linear Regression Intuition
What is the P-Value?
Multiple Linear Regression in Python
Importing the libraries
Importing the dataset
Encoding categorical data
Avoiding the Dummy Variable Trap
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Multiple Linear Regression to the Training set
Predicting the Test set results
Multiple Linear Regression in Python - Backward Elimination
Multiple Linear Regression in Python - Automatic Backward Elimination
Multiple Linear Regression in R
Importing the dataset
Encoding categorical data
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Multiple Linear Regression to the Training set
Predicting the Test set results
Multiple Linear Regression in R - Backward Elimination
Multiple Linear Regression in R - Automatic Backward Elimination
Section 06 - Polynomial Regression
Polynomial Regression Intuition
Polynomial Regression in Python
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Linear Regression to the dataset
Fitting Polynomial Regression to the dataset
Visualising the Linear Regression results
Visualising the Polynomial Regression results
Visualising the Polynomial Regression results (for higher resolution and smoother curve)
Predicting a new result with Linear Regression
Predicting a new result with Polynomial Regression
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Linear Regression to the dataset
Fitting Polynomial Regression to the dataset
Visualising the Linear Regression results
Visualising the Polynomial Regression results
Visualising the Regression Model results (for higher resolution and smoother curve)
Predicting a new result with Linear Regression
Predicting a new result with Polynomial Regression
Section 07 - Support Vector Regression (SVR)
SVR Intuition
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting SVR to the dataset
Predicting a new result
Visualising the SVR results
Visualising the SVR results (for higher resolution and smoother curve)
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting SVR to the dataset
Predicting a new result
Visualising the SVR results
Visualising the SVR results (for higher resolution and smoother curve)
Section 08 - Decision Tree Regression
Decision Tree Regression Intuition
Decision Tree Regression in Python
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Decision Tree Regression to the dataset
Predicting a new result
Visualising the Decision Tree Regression results (higher resolution)
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Decision Tree Regression to the dataset
Predicting a new result with Decision Tree Regression
Visualising the Decision Tree Regression results (higher resolution)
Plotting the tree
Section 09 - Random Forest Regression
Random Forest Regression Intuition
Random Forest Regression in Python
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Random Forest Regression to the dataset
Predicting a new result
Visualising the Random Forest Regression results (higher resolution)
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Random Forest Regression to the dataset
Predicting a new result with Random Forest Regression
Visualising the Random Forest Regression results (higher resolution)
Section 10 - Evaluating Regression Models Performance
R-Squared Intuition
Adjusted R-Squared Intuition
Evaluating Regression Models Performance
Interpreting Linear Regression Coefficients
Section 11 - Regularization Methods
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