Q6. Explain the importance of empathy in workplace communication.
Q7. List two strategies for managing digital distractions while working.
Q8. What role does market research play in entrepreneurship?
Q9. Explain the concept of digital footprint and its importance.
Q10. How does goal setting contribute to self-motivation?
A6. Empathy in workplace communication fosters understanding, builds trust, and enhances collaboration by considering others’ perspectives, feelings, and needs, leading to better relationships and productivity.
A7. 1. Use time-management tools like the Pomodoro Technique to focus on tasks.
2. Turn off unnecessary notifications or set specific times for checking digital devices.
3.Create a dedicated workspace free from distractions to maintain focus and productivity.
A8. Market research helps entrepreneurs understand customer needs, identify trends, assess competition, and make informed decisions to develop effective strategies and minimize risks.
A9. A digital footprint is the trail of data left online. It’s important because it reflects your online reputation and impacts personal, professional, and cybersecurity aspects.
A10. Goal setting provides direction, creates a sense of purpose, and breaks tasks into manageable steps, which boosts self-motivation and drives sustained effort toward achieving objectives.
Q11. Explain how AI helps in email spam detection.
Q12. What is the difference between supervised and unsupervised learning?
Q13. How does facial recognition technology work?
Q14. Explain the concept of “bias” in AI systems.
Q15. What is the role of training data in machine learning?
Q16. How does natural language processing help in machine translation?
A11. AI helps in detecting spam emails by looking for patterns, specific words, and sender behavior. It learns over time to block unwanted emails more accurately.
A12. Supervised learning uses labeled examples to teach a model, while unsupervised learning finds patterns in data that has no labels or predefined categories.
A13. Facial recognition works by analyzing and mapping a person’s facial features and then comparing them to stored data to identify or verify someone.It uses Computer vision.
A14. Bias in AI happens when the data used to train the system is unfair or incomplete, causing the AI to make wrong or unfair decisions.
A15. Training data is like examples that help an AI model learn how to recognize patterns and make better predictions or decisions. Training data is fed into the machine during its training.
A16. Natural language processing helps translate languages by understanding the meaning and grammar of sentences in one language and converting them into another language.
Q17. Explain the ethical considerations in AI development and deployment.
Q18. Describe the process of text preprocessing in NLP applications.
Q19. How do recommendation systems work? Explain with examples.
Q20. Explain the concept of deep learning and its applications.
Q21. Calculate the accuracy and precision for a model with the following results:
True Positives: 85
True Negatives: 90
False Positives: 15
False Negatives: 10
A17. Ethical considerations in AI development include ensuring fairness, transparency, and privacy. AI should not be biased or harm people. Developers must be responsible for how AI is used, ensuring it benefits everyone and does not discriminate based on race, gender, or other factors. It’s important to make sure AI respects people’s privacy and follows ethical guidelines in decision-making.
A18. Text preprocessing in NLP involves cleaning and preparing text data before analysis. This includes removing stop words (common words like “the” or “is”), punctuation, and special characters. It may also include converting text to lowercase, stemming (reducing words to their base form), and tokenizing (splitting text into words or sentences) to make it easier for machines to understand and process.
A19. Recommendation systems suggest products, services, or content based on users’ preferences. They work by analyzing data from users’ previous actions. For example, on Netflix, if you watch a lot of action movies, the system will recommend more action films. These systems can use collaborative filtering (suggesting based on what similar users liked) or content-based filtering (suggesting similar items to what you’ve already liked).
A20. Deep learning is a type of machine learning that uses neural networks to model complex patterns in large amounts of data. It can automatically learn features from data without much human input. Deep learning is used in applications like image recognition, speech recognition, and self-driving cars. For example, it helps Google Photos recognize faces or allows voice assistants like Siri to understand commands.
A.21
