In this course we learn about Supervised ML algorithms, working of the algorithms and their scope of application – Regression and Classification.

Multiple Variable Linear regression

Linear Regression is one of the most popular ML algorithms used for predictive analysis in Machine Learning, resulting in producing the best outcomes. It is a technique assuming a linear relationship between the independent variable and dependent variable.

Multiple regression

Multivariate Regression is a supervised machine learning algorithm involving multiple data variables for analysis. It is used for predicting one dependent variable using various independent variables. This module will drive you through all the concepts of Multiple Regression used in Machine Learning.

Logistic regression

Logistic Regression is one of the most popular ML algorithms, like Linear Regression. It is a simple classification algorithm to predict the categorical dependent variables with the assistance of independent variables. This module will drive you through all the concepts of Logistic Regression used in Machine Learning.

K-NN classification

k-NN Classification or k-Nearest Neighbours Classification is one of the most straightforward machine learning algorithms for solving regression and classification problems. You will learn about the usage of this algorithm through this module.

Naive Bayes classifiers

Naive Bayes Algorithm is used to solve classification problems using Baye’s Theorem. This module will teach you about the theorem and solving the problems using it.

Support vector machines

Support Vector Machine or SVM is also a popular ML algorithm used for regression and classification problems/challenges. You will learn how to implement this algorithm through this module.