Machine Learning

Machine Learning in R and Python

Part 3 - Classification

Section 14 - Logistic Regression

Logistic Regression Intuition

Logistic Regression in Python
- Importing the libraries
- Importing the dataset
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Logistic Regression to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Logistic Regression in R
- Importing the dataset
- Encoding the target feature as factor
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Logistic Regression to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Section 15 - K-Nearest Neighbor (K-NN)

K-NN Intuition

K-NN in Python
- Importing the libraries
- Importing the dataset
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting K-NN to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

K-NN in R
- Importing the dataset
- Encoding the target feature as factor
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting K-NN to the Training set and Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Section 16 - Support Vector Machine (SVM)

SVM Intuition

SVM in Python
- Importing the libraries
- Importing the dataset
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting SVM to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

SVM in R
- Importing the dataset
- Encoding the target feature as factor
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting SVM to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Section 17 - Kernel SVM

Kernel SVM Intuition
Mapping to a higher dimension
The Kernel Trick
Types of Kernel Functions

Kernel SVM in Python
- Importing the libraries
- Importing the dataset
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Kernel SVM to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Kernel SVM in R
- Importing the dataset
- Encoding the target feature as factor
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Kernel SVM to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Section 18 - Naive Bayes

Bayes Theorem
Naive Bayes Intuition

Naive Bayes in Python
- Importing the libraries
- Importing the dataset
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Naive Bayes to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Naive Bayes in R
- Importing the dataset
- Encoding the target feature as factor
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting SVM to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Section 19 - Decision Tree Classification

Decision Tree Classification Intuition

Decision Tree Classification in Python
- Importing the libraries
- Importing the dataset
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Decision Tree Classification to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Decision Tree Classification in R
- Importing the dataset
- Encoding the target feature as factor
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Decision Tree Classification to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Plotting the tree

Section 20 - Random Forest Classification

Random Forest Classification Intuition

Random Forest Classification in Python
- Importing the libraries
- Importing the dataset
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Random Forest Classification to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Random Forest Classification in R
- Importing the dataset
- Encoding the target feature as factor
- Splitting the dataset into the Training set and Test set
- Feature Scaling
- Fitting Random Forest Classification to the Training set
- Predicting the Test set results
- Making the Confusion Matrix
- Visualising the Training set results

Visualising the Test set results

Choosing the number of trees

Section 21 - Evaluating Classification Models Performance

False Positives & False Negatives
Confusion Matrix
Accuracy Paradox
CAP Curve
CAP Curve Analysis

Section 22 - Part Recap

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