ml

MACHINE LEARNING

Exam Instructions:

Total Number of Questions: 45
You will be required to answer a total of 45 multiple-choice questions.

Time Limit: 15 minutes for the entire exam
 Once the time is up, the exam will automatically submit.

Passing Criteria:
A minimum score of 50% is required to pass the exam 

Multiple Attempts:
You are allowed to take the exam multiple times. Only your highest score will be considered for certification.

Additional Instructions:

The exam timer cannot be paused once it begins.

Good luck, and feel free to retake the exam to improve your score!


machine learning

MACHINE LEARNING

Test your MACHINE LEARNING skills with a challenging exam designed to evaluate your knowledge in programming, data structures, and algorithms

The certificate will be generated based on the information you provide in the form, so please ensure that all details are entered correctly.

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1) What is the function of “k-nearest neighbors”?

2 / 45

2) What does “precision” measure in classification models?

3 / 45

3) What is cross-validation?

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4) What is the purpose of normalization in ML?

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5) Which method is used to avoid overfitting in machine learning?

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6) What does the term “bagging” mean in ensemble learning?

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7) Which activation function is commonly used in hidden layers of neural networks?

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8) What is the main function of an activation function?

9 / 45

9) What is “ensemble learning”?

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10) What is “data augmentation” in deep learning?

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11) What is the main goal of clustering in machine learning?

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12) What is the purpose of the “rectified linear unit” (ReLU) activation?

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13) What is “one-hot encoding” used for in machine learning?

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14) Which method is used to avoid overfitting in machine learning?

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15) What is “backpropagation” in neural networks?

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16) What is the main benefit of using a convolutional neural network (CNN)?

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17) What is the role of “learning rate” in optimization algorithms?

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18) What is k-fold cross-validation used for?

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19) What is the difference between classification and regression?

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20) What is “t-SNE” (t-Distributed Stochastic Neighbor Embedding) used for?

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21) What is “bagging” in machine learning?

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22) What is the function of a neuron in a neural network?

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23) What does “regularization” prevent in machine learning?

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24) What is “k-fold cross-validation”?

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25) What is “random search” in hyperparameter tuning?

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26) What is “early stopping” in machine learning?

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27) What is the purpose of using a confusion matrix?

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28) What is “dropout” used for in neural networks?

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29) What is the difference between L1 and L2 regularization?

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30) Which algorithm is commonly used for clustering?

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31) What is regularization in machine learning?

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32) What is “validation set” used for in machine learning?

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33) What is “random search” in hyperparameter tuning?

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34) What is the purpose of “dropout” in neural networks?

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35) Which algorithm is used for regression tasks?

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36) What is the difference between supervised and unsupervised learning?

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37) What is the main objective of clustering algorithms?

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38) What is the purpose of “gradient descent”?

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39) What is the purpose of k-nearest neighbors (KNN)?

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40) Which type of model is used for unsupervised learning?

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41) What does the term ‘bias’ mean in machine learning?

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42) What does “ensemble learning” refer to in machine learning?

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43) What is a latent variable in machine learning?

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44) What is the function of “ReLU” in neural networks?

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45) What is “ridge regression” in machine learning?

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