AI Project Cycle

• What is AI Project Cycle?? 


  The AI Project Cycle is a step-by-step process that a company must follow to derive value from an AI project and to solve the problem.
• Stages of AI Project Cycle

  
  

  There are five stages..

  1. Problem Scoping
  2. Data Acquisition
  3. Data Exploration
  4. Modelling
  5. Evaluation
• Problem Scoping

  To understand a problem, determine the different aspects that affect the problem, and define the project’s goal are problem scoping.
• Problem Scoping

  The 4 W'S Canvas:

  The 4 W’s of Problem Scoping are Who, What, Where, and Why. These 4 W’s help to identify and understand the problem better.

  1. Who – The “Who” element helps us to understand and categorize who is directly and indirectly affected by the problem, and who are known as Stakeholders.
  2. What – The “What” section aids us in analyzing and recognizing the nature of the problem.
  3. Where – What is the situation, and where does the problem arise.
  4. Why – Refers to why we need to address the problem and the advantages for the stakeholders once the problem is solved.
• Problem Scoping

  Statement of the Problem Template...

  After you’ve completed the above 4Ws, make a summary of what you’ve learned. The problem statement template is the name for this summary. This template summarizes all of the important points in one place. So, if the same problem occurs again, this statement will make it much easier to fix
• Data Acquisition


  The method of collecting correct and dependable data to work with is known as data acquisition.

  -> Data can be in the form of text, video, photos, audio, and so on, and it can be gathered from a variety of places such as websites, journals, and newspapers.
• Data is a representation of facts or instructions about an entity that can be processed or conveyed by a human or a machine, such as numbers, text, pictures, audio clips, videos, and so on
• There are two types of data:

  
  

  Structured Data:

  • In a standardized format
  • Has a well-defined structure
  • Follows a consistent order
  • Easily accessible by humans and programs
  • In the form of numbers, characters, special characters, etc Unstructured Data:
  • Information that doesn’t follow traditional data models
  • Difficult to store and manage
  • Examples include video, audio, image files, and log files
• Dataset is a collection of data in tabular format. Dataset contains numbers or values that are related to a specific subject. For example, students’ test scores in a class is a dataset.
• The dataset is divided into two parts....


  1. Training dataset – Training dataset is a large dataset that teaches a machine learning model. Machine learning algorithms are trained to make judgments or perform a task through training datasets. Maximum part of the dataset comes under training data (Usually 80%)
  2. Test dataset – Data that has been identified for use in tests, usually of a computer program, is known as test data. (20% of data used in test data)
• Data Acquisition

  There are six ways to collect data...

  1. Surveys - A research method for gathering data from a predetermined sample of respondents to get knowledge and insights into a variety of issues.
  2. Cameras - We can collect visual data with the help of cameras, this data is unstructured data that can be analyzed via Machine learning
  3. Web Scraping - Web scraping is a technique for collecting structured data from the internet, such as news monitoring, market research, and price tracking.
• Data Acquisition

  There are six ways to collect data...

  1. Observation - Some of the information we can gather through attentive observation and monitoring.
  2. Sensors - With the help of sensors also we can collect the data. A device that detects or measures a physical property is called a sensor, such as a biomatrix.
  3. Application Program Interface - An API is a software interface that enables two apps to communicate with one another.
• Data Exploration

  Data exploration is a technique used to visualize data in the form of statistical methods or using graphs.

  ->Exploration helps you gain a better understanding of a dataset, making it easier to explore and use it later. It also helps to quickly understand the data’s trends, and patterns.