Decision trees are one of the most powerful and widely used supervised models that can either perform regression or classification. In this tutorial, we’ll concentrate only on the classification setting. A decision tree consists of rules […]

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## Introduction to Logistic Regression

In this article, we’ll discuss a supervised machine learning algorithm logistic regression. Logistic regression is a probabilistic classifier. It outputs the probability of a point belonging to a specific class. The output lies between [0,1]. Logistic […]

Continue readingMore Tag## Introduction to Support Vector Machine(SVM)

INTRODUCTION: Support Vector Machine(SVM) is the most popular and powerful supervised machine learning algorithm which is used for both classification and regression. However, it is more popular and extensively used in addressing the classification problems of […]

Continue readingMore Tag## Hierarchical Clustering

Hierarchical clustering is the second most popular technique for clustering after K-means. Remember, in K-means; we need to define the number of clusters beforehand. However, in hierarchical clustering, we don’t have to specify the number of […]

Continue readingMore Tag## Introduction To Linear Regression(Part-2)

In the previous part of the Introduction to Linear Regression, we discussed simple linear regression. Simple linear regression is a basic model with just two variables an independent variable x, and a dependent variable y based […]

Continue readingMore Tag## Introduction to K-means

K-means clustering is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. Before we venture into K-means, let’s first understand what clustering is? What is clustering? The idea behind clustering is […]

Continue readingMore Tag## Introduction to Linear Regression

Before we venture into linear regression, let’s first try to understand what regression analysis is? What is Regression? Regression is a statistical approach used for predicting real values like the age, weight, salary, for example. In […]

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