Univariate Linear Regression in Python Univariate data is the type of data in which the result depends only on one variable. For instance, dataset of points on a line can be considered as a univariate data where abscissa can be considered as input feature and ordinate can be considered as output/result. For example: For …

Simple Linear-Regression using R Linear Regression : It is a commonly used type of predictive analysis. It is a statistical approach for modelling relationship between a dependent variable and a given set of independent variables. There are two types of linear regression. Simple Linear Regression Multiple Linear Regression Let’s discuss Simple Linear regression …

Linear Regression (Python Implementation) This article discusses the basics of linear regression and its implementation in Python programming language. Linear regression is a statistical approach for modelling relationship between a dependent variable with a given set of independent variables. Note: In this article, we refer dependent variables as response and independent variables as features for simplicity. In order to …

Momentum-based Gradient Optimizer introduction Gradient Descent is an optimization technique used in Machine Learning frameworks to train different models. The training process consists of an objective function (or the error function), which determines the error a Machine Learning model has on a given dataset. While training, the parameters of this algorithm are initialized to random …

Optimization techniques for Gradient Descent Gradient Descent is an iterative optimiZation algorithm, used to find the minimum value for a function. The general idea is to initialize the parameters to random values, and then take small steps in the direction of the “slope” at each iteration. Gradient descent is highly used in supervised learning …

Mini-Batch Gradient Descent with Python In machine learning, gradient descent is an optimization technique used for computing the model parameters (coefficients and bias) for algorithms like linear regression, logistic regression, neural networks, etc. In this technique, we repeatedly iterate through the training set and update the model parameters in accordance with the gradient of …

Stochastic Gradient Descent (SGD) What is Gradient Descent? Before explaining Stochastic Gradient Descent (SGD), let’s first describe what Gradient Descent is. Gradient Descent is a popular optimization technique in Machine Learning and Deep Learning, and it can be used with most, if not all, of the learning algorithms. A gradient is the slope of …

Gradient Descent algorithm and its variants Gradient Descent is an optimization algorithm used for minimizing the cost function in various machine learning algorithms. It is basically used for updating the parameters of the learning model. Types of gradient Descent: Batch Gradient Descent: This is a type of gradient descent which processes all the training …

Multiclass classification using scikit-learn Multiclass classification is a popular problem in supervised machine learning. Problem – Given a dataset of m training examples, each of which contains information in the form of various features and a label. Each label corresponds to a class, to which the training example belongs to. In multiclass classification, we have a finite …

Types of Learning – Supervised Learning What is Learning for a machine? A machine is said to be learning from past Experiences(data feed in) with respect to some class of Tasks, if it’s Performance in a given Task improves with the Experience.For example, assume that a machine has to predict whether a customer will buy a specific product lets …

Classification vs Regression Classification and Regression are two major prediction problems which are usually dealt with Data mining and machine learning. Classification is the process of finding or discovering a model or function which helps in separating the data into multiple categorical classes i.e. discrete values. In classification, data is categorized under different labels according …

Types of Regression Techniques When Regression is chosen? A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used, the simplest is the linear regression. It tries to fit data with the best hyperplane which goes through the points. Regression …