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Machine Learning Articles
Page 21 of 56
AI and Robotics: Enhancing Automation and Efficiency in Manufacturing
Welcome to the exciting world of manufacturing with AI and robotics! This essay will detail how AI and robotics are altering the manufacturing scene, the amazing advantages they bring, and the difficulties associated with using such cutting−edge technology. The Evolution of Smart Manufacturing Using Robotics and AI The era when production lines were dominated by physical labor is over. The development of AI and robotics has transformed manufacturing into a field of intelligent, automated procedures. These intelligent devices can carry out tasks with extraordinary accuracy and consistency, opening the door for superior products and more efficient processes. Thanks to the ...
Read MoreNon-Linear SVM in Machine Learning
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 MoreEnsemble Classifier | Data Mining
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 MoreHow to Conduct a Paired Samples T-Test
Introduction In machine learning and data science, many statistical tests are used to compare and find the differences between variables or the features of the data. These tests are mainly hypothesis tests where the conditions are defined, and according to the different tests being conducted, the relationship between variables is assumed. The t-test is also a type of statistical test that is used to compare the means of different groups of the categorical variable. In this article, we will discuss the paired t-test, which is an extension or a type of t-test used in statistics, and we will ...
Read MoreImproving Business Decision-Making Using Time Series
Introduction Time series is one of the widely used in machine learning and data science, used to forecast and analyze the data collected with time components. It is a field of intelligence where the data can be forecasted and analyzed with the help of past data collected. In industry, businesses are using the time series analyzed and related methods to improvise their decision-making process. In this article, we will discuss the ways in which the time series can help improve the decision-making process in the industry and how businesses are using the same to enhance their productivity and ...
Read MoreWhat is Padding in Neural Networks
Introduction Padding is one of the most used concepts in neural networks while working with convolutional neural networks. It is a most known concept to every neural network engineer and deep learning engineer to efficiently extract useful information from the given dataset in deep learning. In this article, we will discuss padding, what it is, why we need padding in neural networks, what is the significance of the same, and how we can apply padding in neural networks with code examples. This article will help one to understand padding from scratch and apply it when necessary. What is ...
Read MoreMachine Learning Engineer vs. Data Scientist: Which is Better?
Introduction Data Science and machine learning are the trending fields in current business scenarios, where almost all kinds of product and service-based companies are leveraging Machine learning and data science techniques to enhance their productivity and advance their workflows. In such cases, many data aspirants are trying to enter the field, but the issue here is with the role. As one single individual can not master all the fields in AI and hence the need for selection of roles comes, which becomes very confusing but important for the career. In this article, we will discuss the machine ...
Read MoreHow Does TVF Make Profit Using Data Science
Introduction Most companies and businesses are leveraging and integrating data science and machine learning techniques into their workflow to enhance their sales, marketing, and productivity of the projects and workings on the same. The viral fever, or the TVF, is one of the biggest content creation companies which creates movies, web series, and serials, which is India based company. The TVF uses data science and machine learning techniques to enhance its productivity and user experience. In this article, we will discuss how TVF makes a profit using data science and machine learning, which techniques they might be using, ...
Read MoreThe Role of Trial and Error in Data Analysis
Introduction Data analysis is an approach in the field of data science and machine learning where the dataset is analyzed well in order to get the relationship between dataset features and get an idea about the behavior of the data and its parameters. In data analysis, trial and error play a major role while developing a machine learning model. It has certain advantages that allow data analysts or data scientists to make the model more reliable and predictive according to the dataset available. In this article, we will discuss the role of trial and error in data analysis, ...
Read MoreHow Does Treating Categorical Variables as Continuous Benefits?
Introduction In machine learning, the performance and accuracy of the model completely depend n the data that we are feeding to it, and hence it is the most influential parameter in model training and model building. Mainly while dealing with the supervised machine learning problems, we have mostly categorical and continuous variables in the dataset. There are some benefits of converting categorical variables into continuous variables. In this article, we will discuss some of the benefits of converting categorical variables to continuous variables, how it affects the model's performance, and what is the core idea behind doing so. ...
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