Talk Data might be facts, statistics, opinions, or any kind of content that is recorded in some format. This could include voices, photos, names, and even dance moves! It surrounds us and shapes our experiences, decisions, and interactions. For example: • Your search recommendations, Google Maps history are based on your previous data. • Amazon's personalized recommendations are influenced by your shopping habits. • Social media activity, cloud storage, textbooks, and more are all forms of data. It is often referred to as the "new oil" of the 21st century. Did you know? 90% of the world's data has been created in just the last 2 years, compared to the previous 6 million years of human existence. Type of Data • Structured Data • Unstructured Data • Semi-structured Data Structured data is like a neatly arranged table, with rows and columns that make it easy to understand and work with. It includes information such as names, dates, addresses, and stock prices. Because of its organized nature, it is straightforward to analyze and manipulate, making it a preferred format for many data-related tasks. On the other hand, unstructured data lacks any specific organization, making it more challenging to analyze compared to structured data. Examples of unstructured data include images, text documents, customer comments, and song lyrics. Since unstructured data does not follow a predefined format, extracting meaningful insights from it requires specialized tools and techniques. Semi-structured data falls somewhere between structured and unstructured data. While not as organized as structured data, it is easier to handle than unstructured data. Semi-structured data uses metadata to identify certain characteristics and organize data into fields, allowing for some level of organization and analysis. An example of semi- structured data is a social media video with hashtags used for categorization, blending structured elements like hashtags with unstructured content like the video itself. b. Natural Language Processing: It refers to the field of computer science and AI that focuses on teaching machines to understand and process languages in both written and spoken form, just like humans do. The goal of an NLP-Trained model is to be capable of “understanding” the contents of documents, including the slangs, sarcasm, inner meaning, and contextual definitions of the language in which the text was written. Differences Between NLP, NLU, and NLG?-generation Natural Language Processing (NLP): This is the broad umbrella term encompassing everything related to how computers interact with human language. Think of it as the "what" what computers can do with human language. It is like the whole library - filled with different tools and techniques for working with language data. Natural Language Understanding (NLU): This is a subfield of NLP that focuses on understanding the meaning of human language. It analyzes text and speech, extracting information, intent, and sentiment. NLU helps computers understand the language and what it means. Imagine finding a specific book in the library. Natural Language Generation (NLG): This is another subfield of NLP, but instead of understanding, it focuses on generating human language. It takes structured data as input and turns it into coherent and readable text or speech. Think of this as writing a new book based on the information gathered in the library. c. Computer Vision: Computer Vision is like giving computers the ability to see and understand the world through digital images and videos, much like how humans use their eyes to perceive their surroundings. In this domain, computers analyze visual information from images and videos to recognize objects, understand scenes, and make decisions based on what they "see." When we take a digital image, it is essentially a grid of tiny colored dots called pixels. Each pixel represents a tiny portion of the image and contains information about its color and intensity. Resolution is expressed as the total number of pixels along the width and height of the image. For example, an image with a resolution of 1920x1080 pixels has 1920 pixels horizontally and 1080 pixels vertically. Higher resolution images have more pixels, providing more detail. Now, here's where AI comes in. To make sense of these images, computers convert them into numbers. They break down the image into a series of numbers that represent the color and intensity of each pixel. This numerical representation allows AI algorithms to process the image mathematically and extract meaningful information from it. For instance, AI algorithms might learn to recognize patterns in these numbers that correspond to specific objects, like cars or faces. By analyzing large amounts of labeled image data, AI systems can "learn" to identify objects accurately.
