Introduction Corpus is known as a collection of text documents that are readable by a machine. Corpus has a particular structure to it. Various NLP operations can be performed on a corpus. Corpus readers are utilities that can read through these text files. Custom corpus is generated using NLTK data ... Read More

Introduction Logistic Regression is a classification algorithm commonly used our machine learning for binary classification. Although the term "Regression" is in its name it is in fact a classification algorithm. It uses log odds with log loss or cross−entropy loss as the cost function. In this article let us see ... Read More

Introduction In recent years Generative Adversarial Networks have gained a lot of traction and used in numerous use cases. GANs are deep neural network architectures used to generate realistic synthetic data from existing data sets. GANs have two sets of neural networks − the first one generates new images and ... Read More

Introduction Robotic Process Automation (RPA) is a technology that can mimic the actions of human beings regarding their interaction with software systems and digital systems. With Robotic Process automation building, managing, and deploying such tools and software becomes extremely easy. Such technology is primarily used by business processes. Robotic Process ... Read More

Introduction Support Vector Machine (SVM) is one of the most popular supervised Machine Learning algorithms for classification as well as regression. The SVM Algorithm strives to find a line of best fit between n−dimensional data to separate them into classes. a new data point can thus be classified into one ... Read More

Introduction Clustering is a technique to group data points into various subgroups such that each point within each subgroup are similar. It is an unsupervised algorithm and there are no labels or ground truth. Mini batch K Means is a variant of the K−Means algorithm that trains from batches at ... Read More

Introduction Ensemble Classifiers are class models that combine the predictive power of several models to generate more powerful models than individual ones. A group of classifiers is learned and the final is selected using the voting mechanism. Data mining is the process of exploring and analyzing large datasets to find ... Read More

Introduction Logistic Regression is the simplest of all classification algorithms in Machine Learning. Logistic Regression uses log loss or cross−entropy loss instead of mean squared error for loss function. Since we already have linear regression why do we need Logistic Regression for classification and why can't use Linear Regression for ... Read More

Introduction The Internet of Things (IoT) is the network of embedded devices, smart devices, and computers infused with sensors that can communicate with each other as well as send and receive packets of data through the network. These devices can communicate with the real world through sensors and can control ... Read More

Introduction Boosting is a class of ensemble modeling algorithms where we build a strong model from several weak models. In boosting all the classifiers are present in series. First, a single model is trained on the actual training data. Then the second classifier is built which is trained on the ... Read More