How to implement K-Nearest Neighbors Algorithm in Python and Scikit-Learn The K-nearest neighbors (KNN) algorithm is a type of supervised machine learning algorithms. KNN is extremely easy to implement in its most basic form, and yet performs quite complex classification tasks. It is a lazy learning algorithm since it doesn’t have a specialized training phase. Rather, …
Introduction to Neural Networks with Scikit-Learn in Python What is a Neural Network? Humans have an ability to identify patterns within the accessible information with an astonishingly high degree of accuracy. Whenever you see a car or a bicycle you can immediately recognize what they are. This is because we have learned over a period …
How to do K-Means Clustering with Scikit-Learn in Python Introduction K-means clustering is one of the most widely used unsupervised machine learning algorithms that forms clusters of data based on the similarity between data instances. For this particular algorithm to work, the number of clusters has to be defined beforehand. The K in the K-means …
Linear Regression using sklearn Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship …
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 …