Python for Machine Learning Qualification related questions

What is Python mainly known for in the context of machine learning? a) Speed b) Scalability c) Flexibility d) All of the above Answer: c) Flexibility Which library is widely used for numerical and scientific computations in Python? a) Pandas b) NumPy c) Scikit-Learn d) TensorFlow Answer: b) NumPy Which of the following is used for data manipulation and analysis in Python? a) NumPy b) Matplotlib c) Pandas d) Scikit-Learn Answer: c) Pandas Which library provides tools for data visualization in Python? a) NumPy b) Pandas c) Matplotlib d) TensorFlow Answer: c) Matplotlib What does the term “machine learning” refer to? a) The ability of machines to think like humans b) The process of teaching machines to perform tasks without explicit programming c) The automation of repetitive tasks d) The analysis of data using statistical methods Answer: b) The process of teaching machines to perform tasks without explicit programming Which of the following is an example of supervised learning? a) Clustering b) Classification c) Anomaly detection d) Reinforcement learning Answer: b) Classification In machine learning, what does “training data” refer to? a) Data used to test the model b) Data used to make predictions c) Data used to train the model d) Data used to validate the model Answer: c) Data used to train the model Which library in Python provides machine learning algorithms and tools? a) TensorFlow b) Pandas c) NumPy d) Matplotlib Answer: a) TensorFlow What does SVM stand for in machine learning? a) Simple Vector Machine b) Support Vector Machine c) Supervised Vector Machine d) Structured Vector Model Answer: b) Support Vector Machine What is the purpose of cross-validation in machine learning? a) To test the model on unseen data b) To train the model with different parameters c) To validate the model’s performance d) All of the above Answer: d) All of the above Which of the following is a classification metric in machine learning? a) R-squared b) Mean Absolute Error c) Precision d) Root Mean Squared Error Answer: c) Precision What is the role of activation functions in neural networks? a) They convert input data into numeric values b) They determine the output of a neuron c) They perform data preprocessing d) They evaluate the performance of a model Answer: b) They determine the output of a neuron What is logistic regression used for in machine learning? a) Classification b) Regression c) Clustering d) Dimensionality reduction Answer: a) Classification Which algorithm is used for unsupervised learning and clustering? a) Decision Trees b) K-Means c) Random Forest d) Naive Bayes Answer: b) K-Means What is the purpose of feature scaling in machine learning? a) To remove outliers from the data b) To normalize the range of independent variables c) To reduce the number of features d) To increase the accuracy of the model Answer: b) To normalize the range of independent variables Which library provides tools for natural language processing (NLP) in Python? a) NLTK b) Scikit-Learn c) TensorFlow d) Pandas Answer: a) NLTK What is the purpose of regularization in machine learning? a) To reduce bias in the model b) To reduce variance in the model c) To reduce overfitting d) All of the above Answer: d) All of the above Which algorithm is suitable for handling missing values in a dataset? a) K-Nearest Neighbors b) Support Vector Machines c) Decision Trees d) Naive Bayes Answer: a) K-Nearest Neighbors Which technique is used for reducing the dimensionality of data? a) Clustering b) Feature Engineering c) Dimensionality Reduction d) Anomaly Detection Answer: c) Dimensionality Reduction Which of the following is an ensemble learning technique? a) Decision Trees b) Support Vector Machines c) Random Forest d) K-Means Answer: c) Random Forest What is the purpose of hyperparameter tuning in machine learning? a) To tune the model parameters for better performance b) To normalize the dataset c) To create new features d) To handle missing values Answer: a) To tune the model parameters for better performance Which method is used for splitting a dataset into training and testing sets? a) train_test_split() b) split_dataset() c) divide_data() d) separate_data() Answer: a) train_test_split() What is the role of the “fit” method in machine learning models? a) It evaluates the model’s performance b) It trains the model on the training data c) It predicts the output values d) It preprocesses the data Answer: b) It trains the model on the training data Which technique is used to handle imbalanced classes in classification problems? a) Oversampling b) Undersampling c) SMOTE d) All of the above Answer: d) All of the above What does the term “overfitting” refer to in machine learning? a) The model performs well on training data but poorly on unseen data b) The model is biased towards certain features c) The model has too many parameters d) The model is undertrained Answer: a) The model performs well on training data but poorly on unseen data Which method is used to evaluate the performance of a classification model? a) Mean Squared Error b) R-squared c) Confusion Matrix d) Root Mean Squared Error Answer: c) Confusion Matrix What is the purpose of batch normalization in deep learning? a) To reduce the computational cost b) To normalize the input data c) To speed up the training process d) To stabilize and improve the training of neural networks Answer: d) To stabilize and improve the training of neural networks Which technique is used for reducing overfitting in neural networks? a) Dropout b) Batch Normalization c) Gradient Descent d) Data Augmentation Answer: a) Dropout What is the role of the “predict” method in machine learning models? a) It evaluates the model’s performance b) It preprocesses the data c) It predicts the output values d) It trains the model Answer: c) It predicts the output values Which of the following is an evaluation metric for regression models? a) Accuracy b) Precision c) Mean Absolute Error d) Confusion Matrix Answer: c) Mean Absolute Error Which method is used to handle categorical data in machine learning? a) Label Encoding b) One-Hot Encoding c) Feature Scaling d) Standardization Answer: b) One-Hot Encoding What is the purpose of feature selection in machine learning? a) To add new features to the dataset b) To reduce the dimensionality of the dataset c) To scale the features d) To normalize the dataset Answer: b) To reduce the dimensionality of the dataset Which algorithm is used for anomaly detection in machine learning? a) Linear Regression b) K-Means c) Isolation Forest d) Naive Bayes Answer: c) Isolation Forest What is the role of learning rate in gradient descent optimization? a) It determines the number of iterations b) It controls the step size during parameter updates c) It selects the best features d) It evaluates the model’s performance Answer: b) It controls the step size during parameter updates