Q.1
Example of Reinforcement learning
  • chess game
  • object recognition
  • Weather conditions
  • price of house
Q.2
Real-Time decisions, Game AI, Learning Tasks, Skill Aquisition, and Robot Navigation are applications in ...
  • Unsupervised Learning: Clustering
  • Supervised Learning: Classification
  • Reinforcement Learning
  • Unsupervised Learning: Regression
Q.3
How do you handle missing or corrupted data in a dataset?
  • Drop missing rows or columns
  • Replace missing values with mean/median/mode
  • Assign a unique category to missing values
  • All of the above
Q.4
Which among the below options are types of Feature engineering? (May choose multiple answers)
  • Replacing missing value
  • Getting mean value from a group of entities
  • Extracting city from home address
  • Changing hyper-parameter values
Q.5
What is overfitting?
  • When a predictive model is accurate but takes too long to run
  • When the model learns specifics of the training data that can't be generalized to a larger data set
  • When you perform hyperparameter tuning and performance degrades
  • When you apply a powerful deep learning algorithm to a simple machine learning problem
Q.6
Which of the folowing algorithm is a lazy learner?
  • K medoids
  • Decision Tree
  • K means clustering
  • K-NN Algorithm
Q.7
Artificial Intelligence is the process that allows computers to learn and make decisions like humans
  • True
  • False
Q.8
Fraud Detection, Image Classification, Diagnostic, and Customer Retention are applications in ...
  • Unsupervised Learning: Clustering
  • Supervised Learning: Classification
  • Reinforcement Learning
  • Unsupervised Learning: Regression
Q.9
How do you choose the root node while constructing a Decision Tree?
  • An attribute having high entropy
  • An attribute having largest information gain
  • An attribute having high entropy and Information gain
  • None of the Mentioned
Q.10
Which of the following is an example of a deterministic algorithm?
  • K-Means
  • PCA
  • Both of these
  • None of these
Q.11
A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.Which of the following statement is true in following case?
  • Feature F1 is an example of nominal variable.
  • Feature F1 is an example of ordinal variable.
  • It doesn’t belong to any of the above category.
  • Both (a) and (b)
Q.12
What kind of learning algorithm for "Future stock prices or currency exchange rates"?
  • Prediction
  • Recognizing Anomalies
  • Generating Patterns
  • Recognition Patterns
Q.13
What kind of distance metric(s) are suitable for categorical variables to finding the closest neighbors
  • Euclidean Distance
  • Manhattan distance
  • Minkowski distance
  • Hamming distance
Q.14
Targetted marketing, Recommended Systems, and Customer Segmentation are applications in ...
  • Unsupervised Learning: Clustering
  • Supervised Learning: Classification
  • Reinforcement Learning
  • Unsupervised Learning: Regression
Q.15
Which one in the following is not Machine Learning disciplines?
  • Information Theory
  • Neurostatistics
  • Optimization + Control
  • Physics
Q.16
Because of low bias and high variance , we get _____ model
  • high error
  • perfectly fitting
  • underfitting
  • over fitting
Q.17
KNN is ___________ algorithm
  • Non-parametric and Lazy Learning
  • Parametric and Lazy Learning
  • Parametric and Eager Learning
  • Non-parametric and Eager Learning
Q.18
Which of the following is not type of learning?
  • Semi-unsupervised Learning
  • Unsupervised Learning
  • Supervised Learning
  • Reinforcement Learning
Q.19
______ is a classification algorithm used to assign observations to a discrete set of classes.
  • Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Classification
Q.20
What kind of learning algorithm for "Facial identities or facial expressions"?
  • Recognizing Anomalies
  • Prediction
  • Generating Patterns
  • Recognition Patterns
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