Machine Learning

This subset focuses on building systems that learn or improve performance based on the data they consume.

Discover the specifics of each method associated with building powerful machine learning models from scratch, making predictions and solving problems easily

Simple Linear Regression

This function models the relationship between two continuous variables. The objective is to predict the value of an output variable based on the value of an input variable.

Simple Linear Regression (ML_R_1)

Multiple Linear Regression

This function models the relationship between more independent variables. The objective is to predict the value of an output variable based on the value of input variables.

Multiple Linear Regression (ML_R_2)

Polynomial Linear Regression

This function uses the relationship between variables to find the best non-linear fit through the data points.

Polynomial Linear Regression (ML_R_3)

Support Vector Regression

This function can be used for solving both linear and non-linear problems.

Support Vector Regression (ML_R_4)

Decision Tree Regression

This function splits the data into smaller subsets while at the same time an associated decision rule is used to predict the target variable, built in the form of a tree structure.

Decision Tree Regression (ML_R_5)

Random Forest Regression

This function builds multiple decision trees and combines their outputs to make a final prediction.

Random Forest Regression (ML_R_6)

Logistic Regression

This function performs analysis on a dataset and returns the predicted binary outcome based on the input independent variables.

Logistic Regression (ML_CN_1)

K-Nearest Neighbours

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

K-Nearest Neighbors - KNN (ML_CN_2)

Support Vector Machine

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

Support Vector Machine (ML_CN_3)

Kernel SVM

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

Kernel SVM (ML_CN_4)

Naive Bayes

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

Naive Bayes (ML_CN_5)

Decision Tree Classification

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

Decision Tree Classification (ML_CN_6)

Random Forest Classification

This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.

Random Forest Classification (ML_CN_7)