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# Automated Brute Forcing on web-based login

Brute force attacks work by calculating every possible combination that could make up a password and testing it to see if it is the correct password. As the password&...

# 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 modellin...

# Analysis of test data using K-Means Clustering in Python

This article demonstrates an illustration of K-means clustering on a sample random data using open-cv library. Pre-requisites: Numpy, OpenCV, matplot-lib Let&#x2019;...

# Linear Regression Using Tensorflow

Prerequisites We will briefly summarize Linear Regression before implementing it using Tensorflow. Since we will not get into the details of either Linear Regression ...

# Understanding Logistic Regression

Pre-requisite: Linear Regression This article discusses the basics of Logistic Regression and its implementation in Python. Logistic regression is basically a supervi...

# K means Clustering – Introduction

We are given a data set of items, with certain features, and values for these features (like a vector). The&#xA0;task is to categorize those items into groups. To ach...

# Python Decision Tree Regression using sklearn

Decision Tree is a decision-making tool that uses a flowchart-like tree structure or is a model of decisions and all of their possible results, including outcomes, in...

# Python Implementation of Polynomial Regression

Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degre...

# Principal Component Analysis with Python

Principal Component Analyis is basically a statistical procedure to convert a set of observation of possibly correlated variables into a set of values of linearly unc...

# Saving a machine learning Model

In machine learning, while working with scikit learn library, we need to save the trained models in a file and restore them in order to reuse it to compare the model ...