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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