# Gradient Boosting for Classification Explained…!

In this article we will try to understand how the Gradient Boosting algorithm works for Classification with an example and also python code.

# Understanding Boosting !

This article mainly focuses on Boosting which is most commonly used and very important algorithm in Machine learning.

# K-Nearest Neighbors (KNN) Explained.

This article covers another important algorithm in Machine Learning which is KNN.

# Sampling Techniques..!

Sampling is a technique that allows us to get information about the population based on the statistics from a subset of population, without investigating every individual from the population.

Before we start understanding sampling and it’s techniques, we need to know what population and sample are:

# Population

A population data set contains all members of a specified group (the entire list of possible data values). For example, population can be all the people living in a particular country.

# Sample

A sample data set contains a part, or a subset of a population. The size of the sample is always less than the size of the population from which it is taken. …

# Decision Trees : Part -2

This is continuation of my previous blog , where we have seen the theoretical concept of how splitting is done in Decision Trees. In this article we will check the code implementation of Decision trees. Before starting this I would suggest you to check out my previous blog for better understanding.

2. ID3

3. C4.5

# Decision Trees : Part-1

In this article we will focus on brief introduction about Decision Trees and different ways on how the splitting happens in Decision Trees.

# Multiple Linear Regression Explained!

The purpose of this article is to explain the Multiple Linear Regression algorithm. I would recommend to check my article on Simple Linear Regression before you start reading this for better understanding.

# What is Multiple Linear Regression?

Multiple Linear Regression (MLR), also known simply as multiple regression, is the most common form linear regression analysis. It is a statistical technique that uses several independent variables to predict the outcome of a dependent variable. The independent variables can be continuous or categorical(dummy coded as appropriate).

Multiple Linear Regression is used to estimate the relationship between two or more independent variables and one dependent variable.

Similar to how we have a best fit line in Simple linear regression, we have a best fit plane or hyper-plane in MLR. …

# Understanding Correlation Coefficient.!

The main purpose of this blog is to understand the important topic of statistics called Correlation Coefficient.

## Correlation:

Correlation is the statistical relationship, whether it is causal or not, between two continuous random variables. In simple words by using correlation we can know how two variables are moving. There are 3 types of correlation,

# Simple Linear Regression Explained.

This blog mainly focuses on explaining how a simple linear regression works. You can find the code and the dataset here.

## What is Simple Linear Regression..?

Don’t worry , We will understand the concept with the help of a simple dataset.

## Example :

We need to first import the data.

# Naive Bayes for Beginners…!

This is my first attempt at writing a blog and I hope you like it.

# Purpose of this Post

I have kept everything in plain English without using any jargon. The main purpose is to help you in understanding the Text classification algorithm in Machine Learning-Naive Bayes with a simple example.

# What you can expect

By reading this blog completely you would learn what this algorithm is all about and applications of Naive Bayes in Machine Learning to solve real world problems.