Page 325 - Touhpad Ai

GLOSSARY

1. Artificial Intelligence (AI): Ability of machines to perform tasks requiring human intelligence.
2. AI Ethics: Responsible and fair use of artificial intelligence.
3. Accountability: Responsibility for AI’s actions and results.
4. Algorithm: A set of instructions to solve a specific problem.
5. Binary System: Computer language that uses only 0 and 1.
6. Bias: Unfair outcomes due to unbalanced data or design.
7. Bar Chart: Uses bars to represent categorical data.

8. Computer Vision: Enables computers to interpret visual information from images or videos.
9. Chatbot: A program that interacts with users using NLP.
10. Consent: User permission before data collection.
11. Data: Raw facts or figures used by AI to learn and make decisions.

12. Deep Learning: Subset of ML using neural networks for complex pattern recognition.
13. Data Visualization: Presenting data graphically for better understanding.
14. Data Cleaning: Fixing or removing incorrect and missing data.
15. Dimensionality: Number of features or variables in a dataset.
16. Dashboard: Interface displaying key charts and metrics together.
17. Data Cleaning: Correcting inaccurate or incomplete data.
18. DataFrame: Table-like data structure in Pandas.

19. Duplicate Data: Repeated entries that cause confusion.
20. dropna(): Function that removes missing rows or columns.
21. Data Model: Blueprint showing how data is organized and connected.
22. Dimensional Model: Separates data into facts and dimensions.
23. Dependent Variable: Variable being predicted.
24. Exploratory Data Analysis (EDA): Examining data to find patterns or mistakes.

25. Entity-Relationship (ER) Model: Diagram showing how entities are related.
26. Ethics: Moral principles defining right and wrong actions.
27. Feature Variable: A measurable characteristic used by AI for prediction or classification.
28. Formatting Data: Ensuring consistent structure and format.
29. Fairness: Ensuring AI treats everyone equally.

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