Tag: Linear regression
Gradient Descent for Linear Regression
Gradient Descent is defined as one of the most commonly used iterative optimization algorithm of machine learning to train the machine learning and deep learning models. It helps in finding the local minima of a function. The best way to define the local minima or local maxima of a function using gradient descent is as…
Read MoreLinear Regression for Machine Learning
Linear regression is the statistical technique to find relationship between two or more variables. To predict the values of response (target) variable based on that values of predictors (external / independent variables) we can use linear regression. Simple linear regression is having only one external factor while Multiple liner regression is having more than one…
Read MoreDifference between Linear Regression and Logistic Regression?
Linear regression is a statistical technique that is used to find a relationship between two variables. It is used for predicting continuous values, such as the price of stocks or the number of cars sold in a given month. It is often used in the social sciences to analyze data. Linear regression, also called “ordinary…
Read MoreWhat are the most important supervised and unsupervised algorithms?
Supervised Learning algorithms: K-nearest neighbors Linear regression Naïve Bayes Support vector machines Logistic regression Decision trees and random forests K-nearest neighbors: K-nearest neighbors is a Machine learning technique which comes under supervised learning. This technique can be used for classification or regression problems. In supervised learning, we need to specify a target value for the…
Read MoreWhat is Supervised Learning?
Supervised learning is when you have a teacher that has knowledge of the correct answer and is teaching someone else. This teaching can take any form, from simple human feedback or input to a more complex model that predicts the outcome of future events. Supervised learning requires two things: -Training data: The data is used…
Read More