When training the model – it fits the best line to predict the value of y for a given value of x. X: input training data (univariate – one input variable(parameter)) Hypothesis function for Linear Regression : The regression line is the best fit line for our model. In the figure above, X (input) is the work experience and Y (output) is the salary of a person. So, this regression technique finds out a linear relationship between x (input) and y(output). Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x). Different regression models differ based on – the kind of relationship between dependent and independent variables they are considering, and the number of independent variables getting used. It is mostly used for finding out the relationship between variables and forecasting. Regression models a target prediction value based on independent variables. Linear Regression is a machine learning algorithm based on supervised learning.
Regression and Classification | Supervised Machine Learning.Basic Concept of Classification (Data Mining).Gradient Descent algorithm and its variants.ML | Momentum-based Gradient Optimizer introduction.Optimization techniques for Gradient Descent.ML | Mini-Batch Gradient Descent with Python.Difference between Batch Gradient Descent and Stochastic Gradient Descent.Difference between Gradient descent and Normal equation.ML | Normal Equation in Linear Regression.Mathematical explanation for Linear Regression working.
Linear Regression (Python Implementation).ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys.
0 kommentar(er)
