Machine Learning Basics: Test Your Knowledge on Algorithms and Applications

Explore core concepts in supervised and unsupervised learning along with practical machine learning applications. Ideal for students and data science professionals.

📌 Important Instructions

  • ✅ This is a free test. Beware of scammers who ask for money to attend this test.
  • 📋 Total Number of Questions: 30
  • ⏳ Time Allotted: 30 Minutes
  • 📝 Marking Scheme: Each question carries 1 mark. There is no negative marking.
  • ⚠️ Do not refresh or close the page during the test, as it may result in loss of progress.
  • 🔍 Read each question carefully before selecting your answer.
  • 🎯 All the best! Give your best effort and ace the test! 🚀
Time Left: 00:00
1. Which of the following is a supervised learning algorithm?
  • Linear regression
  • K-means clustering
  • Principal component analysis
  • K-nearest neighbors
2. What does the "training set" in machine learning refer to?
  • The data used to evaluate the model's performance
  • The data used to train the model and make predictions
  • The data used to test the algorithm's ability to generalize
  • The data used to fine-tune the model after training
3. Which algorithm is used in "unsupervised learning"?
  • K-means clustering
  • Logistic regression
  • Naive Bayes
  • Linear regression
4. Which of the following is NOT a type of machine learning algorithm?
  • Reinforcement learning
  • Supervised learning
  • Unsupervised learning
  • Predictive learning
5. What is the primary goal of a classification algorithm in machine learning?
  • To predict a continuous value
  • To categorize data into distinct classes
  • To group similar data points into clusters
  • To reduce the dimensions of data
6. Which of the following is an example of a regression algorithm?
  • Linear regression
  • Decision tree
  • K-nearest neighbors
  • Support vector machine
7. What does the "bias" term in a machine learning model refer to?
  • The error between predicted and actual values
  • A model's tendency to learn a simple hypothesis
  • A constant value that helps the model make predictions
  • The total amount of data available for training
8. What does "feature engineering" refer to in machine learning?
  • Choosing the right algorithm for the problem
  • Creating new features from the existing data to improve model performance
  • Tuning the model's hyperparameters
  • Evaluating the model's accuracy
9. Which of the following is true about "k-fold cross-validation"?
  • It splits the dataset into 'k' subsets and trains the model on each subset
  • It is used to measure the accuracy of a classification model
  • It always splits the data in half for training and testing
  • It increases the computational cost of training the model
10. What does "gradient descent" help optimize in machine learning algorithms?
  • The features used in the model
  • The hyperparameters of the model
  • The model's weights or coefficients
  • The amount of data used for training
11. Which of the following algorithms is commonly used for clustering?
  • K-means clustering
  • Linear regression
  • Support vector machine
  • Naive Bayes
12. Which algorithm is commonly used for classification tasks?
  • Logistic regression
  • K-means clustering
  • Principal component analysis
  • Support vector regression
13. What is the "support vector" in a Support Vector Machine (SVM)?
  • The points that represent the training set
  • The points that define the boundary of the data's feature space
  • The points that help to measure the model's accuracy
  • The data points that are closest to the hyperplane
14. What does "ensemble learning" involve?
  • Combining multiple models to improve prediction accuracy
  • Using a single model to predict both classification and regression tasks
  • Training models on different subsets of the data
  • Reducing the number of features in the dataset
15. What is the function of the "kernel trick" in a Support Vector Machine?
  • To transform data into higher dimensions to find a better separating hyperplane
  • To create more complex decision boundaries for better classification
  • To normalize the features for better model performance
  • To split the data into two classes
16. What does "dimensionality reduction" refer to in machine learning?
  • Modifying the learning rate of the model
  • Increasing the number of data points for training
  • Reducing the number of features in the dataset to improve model efficiency
  • Selecting the best algorithm for training
17. Which of the following is an advantage of using decision trees?
  • They are easy to interpret and visualize
  • They require a lot of data preprocessing
  • They perform well with unstructured data
  • They are computationally expensive
18. What does the term "feature selection" refer to in machine learning?
  • Selecting the right algorithm for the problem
  • Identifying the most relevant features in the data to improve model accuracy
  • Reducing the amount of data used for training
  • Dividing the dataset into training and test sets
19. What is the main objective of supervised learning?
  • To learn from labeled data and make predictions on unseen data
  • To group similar data points without labels
  • To create a model that evolves through interaction with its environment
  • To reduce the dimensions of the dataset
20. Which machine learning algorithm is used for classification tasks?
  • K-means clustering
  • Logistic regression
  • Principal component analysis
  • Linear regression
21. In which situation would you use a regression algorithm?
  • When you need to classify objects into categories
  • When predicting a continuous numerical value
  • When reducing the dimensionality of data
  • When grouping similar data points into clusters
22. Which of the following methods is used to evaluate a classification model's performance?
  • Mean squared error
  • R-squared
  • Confusion matrix
  • F1-score
23. What does "feature scaling" help achieve in machine learning?
  • It reduces the number of features used by the model
  • It ensures that all features have the same range of values
  • It creates more features to improve model performance
  • It helps split the data into training and test sets
24. What does "bagging" stand for in ensemble methods?
  • Bootstrapped aggregation
  • Balanced aggregation
  • Binned aggregation
  • Basic aggregation
25. Which of the following algorithms is used for dimensionality reduction?
  • K-means clustering
  • Support vector machines
  • Principal component analysis
  • Logistic regression
26. What is the purpose of regularization in machine learning?
  • To reduce overfitting by penalizing large model coefficients
  • To increase the complexity of the model
  • To enhance the model's ability to generalize to unseen data
  • To speed up the training process
27. What does the "learning rate" control in a machine learning model?
  • The number of iterations the model runs
  • The size of the data batches used in training
  • How quickly the model adjusts to the loss function
  • The number of features to include in the model
28. Which of the following is true about Support Vector Machines (SVM)?
  • They are used for classification and regression tasks
  • They can only perform classification tasks
  • They do not work well with non-linear data
  • They require less computational power compared to other algorithms
29. What does "deep learning" refer to in machine learning?
  • Using complex neural networks with multiple layers for learning
  • A method to reduce the size of the dataset
  • A classification technique for small datasets
  • A supervised learning technique with few parameters
30. Which technique can be used to prevent overfitting in neural networks?
  • Cross-validation
  • Regularization
  • Grid search
  • Feature scaling

Explore

IT Fundamentals

Professional Development

Psychology Insights