AI Development

Abstract Feature engineering is often regarded as the secret sauce behind high-performing machine learning models. It involves the art and science of selecting, transforming, and creating variables (features) that improve the predictive performance of a model. This article offers a comprehensive and research-oriented guide to manual and automated feature engineering, including practical examples, techniques, and … Read more

The workplace is evolving rapidly, with Artificial Intelligence (AI) taking on more collaborative roles alongside human workers. But beyond automation and analytics, a new class of AI is entering the conversation—Emotional AI, also known as affective computing. Unlike traditional systems, Emotional AI doesn’t just process logic and data—it perceives and responds to human emotions. In … Read more

Abstract Data preprocessing is the cornerstone of any successful AI or machine learning pipeline. Often underestimated, the quality of data fed into a model directly influences its predictive performance. This article delves into the complexities and techniques of data preprocessing, including cleaning, normalization, transformation, feature engineering, and augmentation. With real-world applications and examples, it offers … Read more

Abstract In the evolving landscape of Artificial Intelligence (AI), the choice of development stack plays a critical role in determining the success and scalability of machine learning projects. This article explores the key components of a modern AI stack—focusing on programming languages, frameworks, and libraries—as of 2025. Through an in-depth comparison of tools such as … Read more