As machine learning practitioners, we come across a wide array of machine learning algorithms that we may exert to build a particular predictive model. In this article, I will be focusing on one of the most sophisticated learning algorithm known as K Nearest Neighbour. This algorithm can be used for dealing with both regression and classification problems in ML.

In this article I will first try to give you an intuition of what the algorithm is, and how it makes predictions. …

Have you ever wondered how your email service provider classifies a mail as spam or not spam almost immediately after you have received it? Or have you thought how the recommendations by online e-commerce platforms changes so quickly depending on real-time user actions. These are some of the use cases where Naïve Bayes classifier is put into action.

Naive Bayes is a supervised classification algorithm that is used primarily for dealing with binary and multi-class classification problems, though with some modifications, it can also be used for solving regression problems. …

Supervised Learning is an essential part of Machine Learning. Classification techniques are used when the variable to be predicted is categorical. A common example of classification problem is trying to classify an Iris flower among its three different species.

Logistic regression is a classification technique borrowed by machine learning from the field of statistics. Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. …

SVMs or Support Vector Machines are one of the most popular and widely used algorithm for dealing with classification problems in machine learning. However, the use of SVMs in regression is not very well documented. This algorithm acknowledges the presence of non-linearity in the data and provides a proficient prediction model.

In this article, I will first try to give you an intuitive understanding of the algorithm by taking a deep-dive into the theory behind the algorithm. Then we will build our very own SVM Regressor model. And finally, we will look into some advantages of using Support Vector Regression.

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As machine learning practitioners, we come across a wide array of machine learning algorithms that we may apply to build our model. In this article I will try to give you an intuition of the concepts of how a Random Forest model works from scratch. This algorithm can be used for computing both Regression and classification problems.

First, we will get acquainted with some important terms like Ensemble Learning and Bootstrap Aggregation, then I will try to give you an intuition of the algorithm and finally, we will build our very own machine learning model using the Random forest regressor.

In this article we will discuss about decision trees, one of the supervised learning algorithm, commonly referred to as CART that can be used for both regression and classification problems.

As the name suggests, the primary role of this algorithm is to make a decision using a tree structure. To find solutions a decision tree makes a sequential, hierarchical decision about the outcome variable based on the predictor data. A decision tree is generally used while working with non-linear data.

Since the rapid outbreak of COVID-19 pandemic, the virus has swept to more than a hundred countries. Social distancing and lockdown orders have helped contain the spread of the virus but there are no quick fixes and vaccines are expected to take several months if not years to be invented.

Tech Giants around the globe are continuously collaborating with medicos, data scientists and government to leverage the strength of artificial intelligence and machine learning to uncover a way to help fight the pandemic. …

In this article we will be getting introduced to the concepts of Multivariate regression. We will also be discussing about a common problem associated with the algorithm i.e. The Dummy Variable Trap.

First we will be getting familiar with the concepts of Multivariate regression and then we will build our very own multivariate regression model. However before getting started I would recommend you to take a look at my article on Simple Linear Regression for understanding the concepts even better .

Multivariate regression is an extension of simple linear regression. It is used when we want to predict the value…

Simple linear regression and logistic regression are usually the first algorithms people learn in their machine learning journey. Due to their popularity, a lot of beginners even end up thinking that they are the only form of regressions. However, there are innumerable regression algorithms that can be used to build ML models.

In this article, we will be discussing some commonly used regression algorithms namely Polynomial Regression, Stepwise Regression, Lasso Regression, Ridge Regression and ElasticNet Regression.

A regression equation is a polynomial regression equation if the power of independent variable is more than 1. Polynomial Regression is used when a…

In the previous blog post, I tried to give you some intuition about the basics of machine learning. In this article, we will be getting started with our first Machine Learning algorithm, that is Linear Regression.

First, we will be going through the mathematical aspects of Linear Regression and then I will try to throw some light on important regression terms like hypothesis and cost function and finally we will be implementing what we have learned by building our very own regression model.

Regression models are supervised learning models that are generally used when the value to be predicted is…