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)Last updated