Basic Quiz 1 - AI And Machine Learning | Short Series

1. What is Artificial Intelligence (AI)?
A) A type of computer hardware
B) Simulating human intelligence in machines
C) Programming computers to solve arithmetic problems
D) A language used to code software

2. Who proposed the Turing Test to evaluate AI’s intelligence?
A) Alan Turing
B) John McCarthy
C) Andrew Ng
D) Elon Musk

3. What is supervised learning?
A) Learning without any labeled data
B) Learning by receiving feedback in the form of rewards and penalties
C) Learning using labeled data to train the model
D) Learning by analyzing unlabeled data

4. Which of the following is an example of unsupervised learning?
A) Email spam detection
B) Customer segmentation
C) Predicting house prices
D) Playing chess with AI

5. What does reinforcement learning involve?
A) Learning from a static dataset
B) Learning through labeled examples
C) Learning by trial and error to maximize rewards
D) Learning patterns in unstructured data

6. What is overfitting in machine learning?
A) When the model fails to learn patterns in training data
B) When the model performs well on training data but poorly on new data
C) When the model predicts outputs without any patterns
D) When the model underestimates the importance of input features

7. What is the purpose of data normalization?
A) To remove all missing values
B) To bring all data features to a common scale
C) To increase the data size for better learning
D) To encode categorical features as numerical values

8. Which algorithm is best suited for predicting continuous values?
A) Linear Regression
B) K-Means Clustering
C) Decision Trees
D) Support Vector Machines

9. What is a key advantage of neural networks?
A) Easy to interpret the results
B) Ability to learn complex patterns in data
C) Low computational requirements
D) Suitable only for small datasets

10. What does a convolutional neural network (CNN) specialize in?
A) Predicting time-series data
B) Recognizing patterns in images
C) Generating random numbers
D) Detecting anomalies in large datasets