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Numeric data can be further classified as:

                                Continuous Data                                       Discrete Data

               Continuous data can take any numeric value within a  Discrete data refers to distinct single values. It
               specified range.                                    consists of whole numbers without decimal parts
                                                                   that represent distinct categories or values.
               Continuous data is measurable.                      Discrete data is countable.
               This type of data can be infinitely subdivided and   Discrete data cannot be subdivided meaningfully.
               often includes decimal points.

               Often used to analyse using statistical techniques   It is used to analyse  using frequency distributions,
               such as mean, median, standard deviation, and       bar charts, and probability distributions.
               correlation.
               Examples: dimensions of classroom, height, weight,   Examples: number of girls and boys in class, number
               temperature, time, etc.                             of subjects in class 9th, count of anything.



              Qualitative Data versus Quantitative Data

                               Quantitative Data                                    Qualitative Data
               Data is depicted in numerical terms.                Data is not depicted in numerical terms.

               Can be shown in numbers and variables like ratio,   Could be about the behavioural attributes of a
               percentage, and more.                               person, or things.
               Examples: 100%, 1:3, 123                            Examples: loud behaviour, fair skin, soft quality, and
                                                                   more.


              AI Domains and Type of Data
              Various types of data are utilised across different domains to train models, make predictions, and generate
              insights. Here are the types of data commonly used in three key domains of AI:


              Natural Language Processing (NLP)

                Natural Language Processing (NLP) is a field of Computer Science and a subfield of Artificial Intelligence that aims
              to make computers understand human language. It's all about teaching, and training computers to understand
              and work with human language. Types of data used in NLP are:
              ●   Textual data: This includes a wide range of written text, such as articles, books, emails, social media posts,
                  web content, PDF files, etc.

              ●   Audio data: Audio recordings of spoken language, which are transcribed into textual data.

              Computer Vision

                Computer Vision is  a field of Artificial Intelligence (AI) that uses machine learning and neural networks to teach
              computers to derive meaningful information from digital images, videos and other visual inputs. It is like giving
              eyes to computers. It helps them look at pictures and videos from the real world and understand what they’re
              seeing. With Computer Vision, computers can figure out what’s in a picture or video, just like we do. They can
              recognise objects, people, and even actions happening in videos.


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