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In this tutorial we will learn how to encode or convert categorical variables in numbers. Categorical variables are generally of 2 types:...

In this tutorial we will understand the concepts of standardisation and normalisation and will learn how to implement them in Python

In this tutorial we will be covering about the concepts and logic of training and test sets.

In this tutorial we would be understanding how to implement Logistic Regression algorithm in Python.

Logistic Regression is used for classification problems which is used to predict the dependent variable which is categorical in nature....

In this tutorial we would explain Hierarchical Clustering - one of the popular unsupervised learning clustering methods.

In this tutorial we would learning how to implement Hierarchical Clustering in Python along with learning how to form business insights..

In this tutorial we would be understanding how to implement Decision Trees algorithm in Python.

In this tutorial we will understand in detail various performance metrics which can be used to evaluate the performance of a model.

In this tutorial we would learning how to implement K - Means Clustering in Python along with learning how to form business insights.

K-Means clustering is an unsupervised learning algorithm, which is used for clustering. This tutorial discusses its in-depth explanation.

Grid Search is used to find the best parameters for a model. In this tutorial we will understand how to implement Grid Search in Python

This article provides detailed explanation of Grid Search, which is widely used for finding the best parameters in a model.

In this tutorial we would be understanding how to implement Random Forests algorithm in Python.

This tutorial covers easiest explanation for Random forests - a bagging algorithm.

This post explains the concepts of bagging and boosting in details.

A detailed tutorial of how to run KNN using Python

To understand what is a decision tree let us consider an example: Suppose a bank wants to decide, whether they want to give loan to a...

K-Nearest Neighbours is a simple algorithm, where the prediction for a new instance is the plurality class among its k closest neighbours.

An easy explanation of K-fold cross validation, with examples

Explaining training and test sets in detail.