Twitter Sentiment Analysis refers to the process of extracting and interpreting emotions, opinions, and attitudes from tweets. Given the platform’s fast-paced, text-driven nature, it offers a rich and immediate source of public sentiment across diverse topics. Businesses, political analysts, and researchers increasingly rely on Twitter sentiment analysis to gauge public opinion in real time. Twitter’s ...
Time series analysis is a powerful technique used to understand trends, patterns, and seasonal variations in data collected over time. It plays a critical role in fields such as finance, weather forecasting, healthcare, energy, and retail, where predicting future values accurately is key to decision-making. With the exponential growth in data availability, mastering time series ...
Understanding the architecture of Tableau is essential for anyone looking to leverage its full potential—whether you’re a developer creating dashboards, an analyst exploring data, or an IT administrator managing deployments. Tableau’s architecture underpins its powerful ability to connect, process, visualize, and share data across organizations. By learning how Tableau’s components interact—both on the desktop and ...
Lasso Regression, short for Least Absolute Shrinkage and Selection Operator, is a linear regression technique that enhances prediction accuracy and model interpretability by performing both variable selection and regularization. Unlike traditional linear regression, which can overfit data when too many features are involved, Lasso adds a penalty to the regression equation to shrink some coefficients ...
Generalization in machine learning refers to a model’s ability to perform well on new, unseen data after being trained on a specific dataset. It determines how effectively a model applies learned patterns to make accurate predictions beyond the training data. A well-generalized model captures meaningful relationships within the data, ensuring reliability across different scenarios. However, ...
Computational Learning Theory (CLT) is a branch of machine learning and theoretical computer science that studies the mathematical principles behind learning algorithms. It focuses on defining how efficiently an algorithm can learn patterns from data and generalize to unseen inputs. CLT provides a formal framework for evaluating machine learning models, answering key questions such as: ...
Maximum Likelihood Estimation (MLE) is a statistical technique used to estimate the parameters of a probability distribution by maximizing the likelihood function. It is widely applied in machine learning, statistics, and AI to optimize models for tasks such as classification, regression, and generative modeling. MLE is commonly used in logistic regression, Gaussian Mixture Models (GMMs), ...
Image classification is a key task in computer vision that involves assigning labels to images based on their content. It enables machines to automatically recognize and categorize objects, patterns, and scenes, making it an essential technology in healthcare, security, retail, and autonomous systems. Machine learning (ML) plays a crucial role in automating image classification, eliminating ...
Image recognition is a computer vision technology that enables machines to identify, classify, and interpret visual content from images or videos. By using machine learning (ML) and deep learning, image recognition allows computers to detect objects, recognize patterns, and extract insights from visual data with human-like accuracy. This technology is revolutionizing industries by enabling automation ...
Standardization in machine learning is a preprocessing technique used to transform numerical features so that they have a mean of zero and a standard deviation of one. This ensures that all features contribute equally to the model, preventing bias caused by different scales of measurement. Standardization is crucial for improving model performance, especially in algorithms ...
Mohit Uniyal
Mayank Gupta
Team Applied AI