AI PROJECT
Q1. What are the key stages of an AI project?
Answer : AI projects follow a structured lifecycle to ensure success. The key stages are:
1. Problem Identification: Define the problem AI will solve.
2. Data Collection: Gather relevant data from various sources.
3. Data Preparation: Clean, preprocess, and transform data for model training.
4. Model Selection: Choose the right AI/ML model based on problem type.
5. Model Training & Testing: Train the model on data and test its performance.
6. Evaluation: Measure accuracy using metrics like precision, recall, and F1-score.
7. Deployment: Integrate the model into an application for real-world use.
Q2. Why is data important in AI projects?
Answer : AI models learn patterns from data to make predictions. If the data is incomplete or biased, the model will be inaccurate.
Example: A self-driving car needs high-quality road and traffic data to function safely.
Q3. What is the role of machine learning in AI projects?
Answer : Machine Learning (ML) is a core component of AI that allows models to improve over time. It identifies patterns from historical data and applies them to new inputs.
Example: A spam filter in emails uses ML to classify messages as spam or not.
Q4. What are the common challenges in AI projects?
Answer : Data-related challenges: Poor-quality or biased data affects accuracy. Computational costs: AI models require significant processing power.
Interpretability: Many AI models work like "black boxes," making it hard to
understand their decisions.
Ethical concerns: AI may lead to job loss, bias, or misuse in surveillance.
Q5. What is a dataset, and why is it needed in AI projects?
Answer : A dataset is a collection of structured or unstructured data used to train AI models.
Example: A facial recognition AI requires a dataset of human faces to learn
different features.
Q6. What is model evaluation in an AI project?
Answer : Evaluation helps determine whether an AI model is performing well.
Common metrics include:
Accuracy: Measures correct predictions.
Precision & Recall: Used for classification tasks.
F1-Score: Balances precision and recall.
Example: In medical diagnosis AI, a high recall ensures fewer false negatives
(missed cases).
Q7. What is the significance of feature selection in AI projects?
Answer : Feature selection involves picking only the most important data attributes for the model.
It reduces computational cost and improves performance.
Example: In a housing price prediction model, "Location" and "Number of
Bedrooms" may be important features, but "House Color" might not be.
Q8. What are ethical concerns in AI projects?
Answer : Bias in AI: If an AI recruiting system is trained on biased data, it may unfairly reject certain candidates.
Data privacy: AI systems processing personal data (like facial recognition) raise concerns.
Job displacement: Automation in industries may lead to unemployment.
Q9. What is AI model deployment?
Answer : Deployment means integrating a trained AI model into an application.
Example: AI chatbots (like Siri or Alexa) use deployed models to interact with users.
Q10. How does AI impact different industries?
Answer : Healthcare: AI assists in diagnosing diseases and robotic surgeries.
Finance: AI detects fraud and automates stock trading.
Retail: AI recommends products based on user preferences.
Manufacturing: AI-powered robots assemble products in factories.
