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 our daily lives, we effortlessly recognize faces and understand voices, tasks that seem almost second nature to us. But explaining how we do these things to machines is not easy. So, how do we make machines think? Can we teach them using examples? Think of it like this: just as we fuel our brains with energy, do we need to feed machine learning algorithms to make them learn? Machine learning models are made up of mathematical structures that allow them to map input to output. Imagine, you want to teach a machine to recognize faces in photos. You'd give it tons of pictures with faces labeled 'face' and pictures without labeled 'face'. The machine learns by looking at these examples, figuring out patterns, and then making its guesses whether a new picture has a face or not. Now, let's dive deeper and understand what an artificial neural network is, drawing inspiration from the intricate workings of biological neurons to construct models that simulate learning processes.
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.