This set of questions covers a range of foundational topics in AI and ML, including supervised and unsupervised learning, model evaluation, and applications in natural language processing and deep learning. These questions are designed to test knowledge across various core concepts in AI/ML, suitable for beginners and those preparing for certification exams in these fields.
A Quiz on Artificial Intelligence and Machine Learning
Score: 0 / 10
Questions
Supervised learning involves training a model on labeled data.
Q1. What is the main objective of supervised learning in machine learning?
In supervised learning, the model is trained to predict outcomes based on labeled input data.
Classification tasks involve categorizing data into predefined classes.
Q2. Which algorithm is commonly used for classification tasks?
Decision Trees are widely used for classification tasks due to their interpretability and ability to handle both numerical and categorical data.
Overfitting is a common problem when a model learns too much from the training data.
Q3. What does "overfitting" refer to in machine learning?
Overfitting occurs when a model is too complex and captures noise in the training data, leading to poor generalization on new data.
Unsupervised learning deals with unlabeled data to find patterns.
Q4. Which of the following is an example of unsupervised learning?
PCA is an unsupervised learning method used for dimensionality reduction and to identify patterns in data without labels.
Learning rate is a key hyperparameter in training neural networks and other models.
Q5. What is the purpose of a "learning rate" in gradient descent optimization?
The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated.
Tokenization is a fundamental step in text preprocessing.
Q6. In natural language processing, what does "tokenization" refer to?
Tokenization involves splitting text into smaller units, such as words or phrases, which can then be analyzed and processed.
Evaluation metrics help measure the effectiveness of a model's predictions.
Q7. Which metric is commonly used to evaluate the performance of a classification model?
The F1 Score is commonly used for evaluating classification models, especially when dealing with imbalanced datasets, as it considers both precision and recall.
CNNs are a type of deep learning model particularly effective in certain applications.
Q8. What is a "convolutional neural network" (CNN) primarily used for?
CNNs are specifically designed for processing structured grid data, like images, and are widely used in image and video recognition tasks.
An agent interacts with an environment to learn optimal actions.
Q9. In reinforcement learning, what is an "agent"?
In reinforcement learning, an agent is an entity that takes actions based on its observations of the environment to maximize cumulative reward.
Regularization techniques help in preventing overfitting.
Q10. What is "regularization" in machine learning?
Regularization adds a penalty for more complex models to prevent overfitting, encouraging simpler models that generalize better.
Similar Tests
🎥 Video Resources
Artificial Intelligence Quiz | 20 Questions | AI Quiz | General Knowledge
Artificial Intelligence Quiz | 20 Questions | AI Quiz | General Knowledge
Quiz on artificial intelligence
🚩 Can You Pass this Machine Learning Quiz? | #machinelearning
AI takes Python Quiz | Machine Learning | Artificial Intelligence #pythonquiz #ai #quiz #coding
AI QUIZ || Test your Knowledge of Artificial Intelligence || AI Puzzle
Artificial intelligence MCQ Questions part1 (Father of AI is John McCarthy, plz correct it)