Found 664 Articles for Machine Learning

Personalized learning and intelligent tutoring systems are innovative concepts in the ever−changing world of education, thanks to the incorporation of artificial intelligence (AI). This innovative strategy uses AI algorithms to provide educational experiences specifically catered to pupils' unique needs and preferences. The traditional educational system may be redesigned by AI−powered personalized learning and intelligent coaching since they provide real−time feedback, adaptable learning pathways, and personalized information. This article examines the substantial effects of these cutting−edge technologies, highlighting their advantages, difficulties, and potential future effects. Comprehending Intelligent Tutoring Systems and Personalized Learning A Customized Journey for Personalized Learning The goal of ... 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 why Logistic Regression is a classification algorithm in nature. Logistic Regression as a Classification Algorithm A Linear Regression algorithm can be given represented by a linear equation such that a univariate regression model with $\mathrm{\alpha_{0}}$ intercept and $\mathrm{\alpha_{1}}$ can be written as $$\mathrm{y=\alpha_{0}+\alpha_{1}x}$$ The line of best fit is ... 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 the second one discriminates between real and generated images. The discriminants give feedback to the generator about the image whether it is real or fake so that the generator can produce better images that look exactly like the real ones. This process is continued till the discriminator fails to differentiate ... 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 Automation − A wider picture Different types of Robotic Process Automation There are three types of RPA software. Attended Robotic Process Automation − It requires very less human intervention Unattended Robotic Process Automation − It required no human intervention Hybrid Robotic Process Automation − It combines both ... 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 of these classes. The SVM algorithm creates two hyperplanes while maximizing the margin between them. The points that lie on these hyperplanes are known as Support Vectors and hence the name Support Vector Machine. The below diagram shows the decision boundary and hyperplanes for an SVM that is used to ... 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 random from memory. In this article let us understand Mini Batch K−Means in detail. Before moving on to Mini Batch K−Means let us have a look at K−Means in general The K−Means clustering approach The K−Means is an iterative approach that tries to group data points into K separate subgroups ... 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 and explore important patterns, relationships, and information. The extracted information can then be used to solve business problems, predict trends and generate strategic plans by organizations. Ensemble classifiers are used in data mining to perform such tasks. Why do we need ensemble classifiers? Ensemble models(classifiers) can solve many problems and ... 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 classification? Let us understand this fact through this article and explore the cost function used in Logistic Regression in detail. Why do we need Logistic Regression and can't use Linear Regression? In Linear Regression, we predict a continuous value. If we fit Linear Regression to the classification task, the line ... 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 or move a system using actuators that are the heart of an IoT system. Machine learning and IoT have a very association in the sense that many organizations using machine learning and Ai based applications rely on terabytes of data captured through IoT and embedded devices. This da can be ... 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 errors produced by the first model and it tries to correct the errors produced by the previous model. This process is repeated continuously and new models are added till there are non−errors and the prediction on training data is accurate or we have reached the maximum threshold of models to ... Read More