Machine Learning Basics: Types and Use Cases Quiz Quiz

1. Definition of Machine Learning

   Which of the following best defines machine learning?

   1. A. Programming computers using only fixed rules
   2. B. Enabling computers to learn patterns from data and improve over time
   3. C. Using manual calculations without computers
   4. D. Building only websites and databases
   5. E. Teaching robots to dance without any data

2. Types of Machine Learning

   In which type of machine learning are algorithms trained on labeled data to predict outcomes for new data?

   1. A. Unsupervised learning
   2. B. Superficial learning
   3. C. Supervised learning
   4. D. Reinforced learning
   5. E. Randomized learning

3. Example of Supervised Learning Use Case

   Which scenario is a typical example of supervised machine learning?

   1. A. Grouping music by genre with no prior labels
   2. B. Predicting house prices based on past sales data with known outcomes
   3. C. Sorting emails without any information about spam
   4. D. Letting a robot explore a maze with no feedback
   5. E. Generating random numbers for a game

4. Unsupervised Learning Example

   Which of the following is an example of unsupervised learning?

   1. A. Diagnosing diseases from labeled patient data
   2. B. Assigning students grades using answer keys
   3. C. Automatically clustering customers into groups based on shopping habits without labels
   4. D. Training a model to recognize voices using transcripts
   5. E. Calculating interest rates for loans

5. Reinforcement Learning Basics

   What is the main idea behind reinforcement learning in machine learning?

   1. A. Learning solely from past mistakes without feedback
   2. B. Maximizing a reward by taking actions in an environment, like a robot learning to walk
   3. C. Training models only on images
   4. D. Not using any data for training
   5. E. Copying answers from a textbook

6. Data in Machine Learning

   Why is data important in machine learning?

   1. A. Data is used to test only hardware components
   2. B. Data helps algorithms learn from examples to make predictions or decisions
   3. C. Data serves only as decoration for reports
   4. D. Data is ignored in most machine learning tasks
   5. E. Data predicts the weather automatically

7. Typical Use Case: Classification

   Which situation best represents a machine learning classification problem?

   1. A. Estimating the value of a car in dollars
   2. B. Grouping colors based on their shades
   3. C. Assigning emails as 'spam' or 'not spam' based on their content
   4. D. Sorting books by their thickness
   5. E. Calculating the sum of numbers

8. Difference Between Training and Testing Data

   What is the primary distinction between training data and testing data in machine learning?

   1. A. Training data is used to teach the model, while testing data evaluates its performance
   2. B. Training data is always smaller than testing data
   3. C. Training data contains only images, and testing data only texts
   4. D. Testing data is used for creating the model from scratch
   5. E. There is no difference between the two

9. Regression Task Example

   Which of the following problems is best solved using regression in machine learning?

   1. A. Predicting whether an animal is a cat or a dog
   2. B. Calculating the average of five numbers
   3. C. Predicting the temperature tomorrow in degrees Celsius
   4. D. Assigning shapes into circles and squares
   5. E. Guessing a random word from a dictionary

10. Outcomes of Machine Learning

    What is a likely outcome when a machine learning model is trained well and provided good data?

    1. A. The model makes more accurate predictions on new, similar data
    2. B. The model ignores the data completely
    3. C. The model always gives random outputs
    4. D. The model refuses to work with numbers
    5. E. The model always guesses the same answer