## Kernel SVM

Sometimes it is difficult to find the linear decision boundary for some classification problems and if we project the data into higher dimension then we will get the hyperplane that helps to classify the data . But Mapping into High Dimension space is highly computational expensive operation. Simple Example- Suppose Read more…

## Simple Linear Regression

In last article we learned about the types of machine learning algorithm. Lets us explore one of those techniques – Simple Linear Regression. In simple linear regression, we need to predict value of one dependent variable on the basis of given independent variable. Throughout this blog, we will use below Read more…

## SVM

SVM (Support Vector Machine) is a supervised machine learning algorithm which is mainly used to classify data into different classes. SVM can be used for both classification and regression problems.Though it is mainly known for classification problems.Unlike most algorithms,SVM makes use of a hyperplane which acts like a decision boundary Read more…

## Correlation

In many practical scenarios, we might come across the situation where observations are available on two or more variables like heights and weights of the person ,expenditure on advertisement and sales revenue, Tweet likes and popularity index of person etc. A natural question arises in mind that is there any Read more…

## Clustering

Clustering – As the word is self explanatory that here we have group of similar points. Clustering is the method of unsupervised learning and is used for statistical data analysis in many fields. Clustering is the process of collection of objects on the basis of similarity and dissimilarity between them. Read more…

## KNN

KNN(K-Nearest Neighbours) is the non-parametric and supervised machine learning algorithm used for classification as well as regression. By non-parametric, we meant that it does not make any assumptions about the distribution of data. Here we are going to discuss KNN in classification problem.KNN can be used in various applications like Read more…

## MultiCollinearity

Multicollinearity It occurs when two or more independent variables in dataset are correlated to each other or to the linear combination of two or more independent variables. Multicollinearity plays an important role during regression analysis. As explained earlier, that regression coefficients are the weightage of impact of corresponding independent variable Read more…

## Linear Regression

In last article we learned about the types of machine learning algorithm. In this article let us explore one of those techniques. Simple linear regression. We learnt that in———– y as a prediction variable depends on data fed to the system (x) according to following equation. Y=f(x) To explain in Read more…

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