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6. Which approach allows users to build, train, and deploy AI models without needing any programming or coding
knowledge?
a. Data Science b. No-Code AI
c. Machine Learning d. Natural Language Processing
7. What does Descriptive Statistics help us identify in data?
a. Complex algorithms b. Patterns, trends, and key points
c. The most complicated parts of data d. Ways to collect more data
8. What is used to load and import data into the platform?
a. Data Analyzer b. File Widget
c. Data Processor d. Report Generator
9. The complete collection of raw data available for a test or experiment is called:
a. A sample b. A variable
c. A dataset d. A population
10. Which of the following tools is an open-source data mining and machine learning tool designed for data analysis,
visualization, and exploration?
a. Microsoft Excel b. Tableau
c. Orange Data Mining d. SPSS
B. Short answer type questions.
1. What are the three main domains of Artificial Intelligence (AI)?
Ans. The three main domains of Artificial Intelligence (AI) are Data Science, Computer Vision, and Natural Language
Processing (NLP).
2. What is Data Science?
Ans. Data Science is a continuous process of exploring and discovering new things by analysing data. It helps us find
patterns, trends, and insights that allow us to better understand the world. Data Science takes raw data and turns it into
valuable knowledge using a mix of statistics, computer techniques, and specialised knowledge from different fields.
3. What do you understand by the term Transform Widgets?
Ans. Transform Widgets are used for modifying and transforming data in your machine learning workflow. They allows users
to apply various preprocessing and feature engineering techniques to manipulate datasets, preparing them for more
advanced analysis or model building. In short we can say that these widgets help perform different operations on data.
4. Name the stages of the AI project cycle.
Ans. The stages of the AI project cycle are:
Stage 1. Problem Scoping
Stage 2. Data Acquisition
Stage 3. Data Exploration
Stage 4. Data Modelling
Stage 5. Evaluation
Stage 6. Deployment
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