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Features and labels are crucial in machine learning. To ensure algorithms can process data, categories must be converted into numerical formats using techniques like one-hot encoding and ordinal encoding. This post provides an overview of both methods, explaining their workings and applications
Two fundamental building blocks of machine learning are features (input) and labels (output). This article explains what features and labels are, their different types, and how they are applied in various machine learning models.
Artificial intelligence (AI) has evolved from a theoretical concept to a transformative technology impacting various industries. This article guides you through the origins of AI, its initial interpretations, and its progression into machine learning (ML) and deep learning (DL). By understanding these foundational concepts, you'll be well-prepared to embark on practical projects and delve deeper into the dynamic world of AI and ML. Join me as we unravel the basics and set the stage for hands-on exploration in the fascinating field of artificial intelligence.
In the rapidly evolving landscape of Artificial Intelligence (AI), the journey from symbolic AI to the emergence of Deep Learning has been marked by significant milestones. This exploration delves into the historical context, the challenges encountered in the early days of AI, and the transformative breakthroughs that paved the way for the prominence of Deep Learning.