Machine Learning

One of the most fascinating problems in machine learning is determining the similarity between two data points. From signature verification and face recognition to recommendation systems, learning to compare and decide similarities is important. A powerful approach to this problem is Similarity Learning using Siamese Networks. Siamese Networks are a type of neural network architecture […]

Contributed by: Arun K Sharma Imagine a large law firm takes over a smaller law firm and tries to identify the documents corresponding to different types of cases such as civil or criminal cases which the smaller firm has dealt or is currently dealing with. The presumption is that the documents are not already classified

Linear discriminant analysis is supervised machine learning, the technique used to find a linear combination of features that separates two or more classes of objects or events.  Linear discriminant analysis, also known as LDA,  does the separation by computing the directions (“linear discriminants”) that represent the axis that enhances the separation between multiple classes. Also, Linear

It’s easy for the human mind to distinguish between different types of fruits by noticing their color and shape. But what if you ask a machine what type of fruit is in an image? Ever wondered what the machine will answer? This is where the concept of multiclass classification is introduced. You can detect the

Bias and Variance  Ensemble Methods Bagging Boosting Bagging vs Boosting Implementation Bias and Variance  For the same set of data, different algorithms behave differently. For example, if we want to predict the price of houses given for some dataset, some of the algorithms that can be used are Linear Regression and Decision Tree Regressor. Both

Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression. It finds the optimal boundary to separate classes, ensuring maximum margin. This article explores SVM’s working, mathematical foundation, types, real-world applications, and implementation with examples.

In every algorithm of machine learning, there is an approach that is unique yet easily interpretable. Logistic regression is one such algorithm with an easy and unique approach. It is very often used in the credit and risk industry for its easy intuition on predicting the chances of default and risk cases. It is indeed