Logistic Regression is a method to create Machine Learning model for two class problems. It came out of linear regression but used to generate binary output (0 and 1) for making classifications. For example In Linear Regression we use simple linear equation as follows :- Yh = b0 + b1X1 Where X combines linearly with […]

I am writing this article in the amid of Covid 19 outbreak. Lot many people are suffering of this diseases and lot many lost their life. Social distancing is one of the key that people are using to avoid this disease as much as possible. As well as mask and sanitisers becomes our daily need. […]

Here we will develop a deep learning model using CNN VGG-16 architecture to predict about Pneumonia using any chest x-ray image with more then 95% of accuracy. With continuous growth in the field of AI and Machine Learning It is stepping into almost every section of industry. In healthcares sector a lot of analysis can […]

Decision Tree Classification Algorithm is used for classification based machine learning problems. It is one of the most popular algorithm as the final decision tree is quite easy to interpret and explain. More advanced ensemble methods like random forest, bagging and gradient boosting are having roots in decision tree algorithm. Here we will try to […]

KNN or K – Nearest Neighbours is one the powerful algorithm used in classification based problems to successfully make categorical predictions. Scikit-Learn gives us built in library to use and make the process easier for us if we are having data. But here we will write KNN code mathematically without any inbuilt library to figure […]

Decision Tree is an algorithm build for Machine learning purposes which works on the concepts of dividing data into subsets. It means it works to give you subsets representing only one type of category or values within particular range. But there are certain drawback that need to be discussed. Editorial Team

In case of Principal Component Analysis we project our data points on a vector in a direction of maximum variance to decrease the number of existing components. In this case we consider the direction eigenvector generated using covariance matrix as the direction of maximum variance. In this article we look into the proof of why […]