Computer Science

A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Semi-supervised learning

A. Increasing the complexity of the model
B. Decreasing the amount of training data
C. Using cross-validation
D. Ignoring outliers in the data

A. When a model performs well on training data but poorly on new data
B. When a model performs well on both training and new data
C. When a model performs poorly on training data but well on new data
D. When a model fails to identify patterns in the data

A. K-means clustering
B. Decision Trees
C. Apriori algorithm
D. Principal Component Analysis

A. Weak AI
B. Strong AI
C. Narrow AI
D. Smart AI

A. Machines being physically assembled by robots
B. Machines using algorithms to improve their performance over time
C. Machines performing pre-programmed tasks
D. Machines that learn languages

A. Speech recognition
B. Image processing
C. Robotics
D. All of the above

A. Alan Turing
B. John McCarthy
C. Marvin Minsky
D. Geoffrey Hinton

A. A field of study that focuses on programming computers to solve complex problems
B. The simulation of human intelligence in machines
C. A branch of mathematics
D. A type of hardware

A. Generate Leads at High Costs
B. Grow Your Customer Base
C. Generate Leads at Low Costs
D. Both B and C

A. Keyword Relevance
B. Landing Page Quality
C. Quality Score
D. All of the above

A. Advertising cost ($) + Ads clicked (#)
B. Advertising cost ($) / Ads clicked (#)
C. Ads clicked (#) / Advertising cost ($)
D. Ads clicked (#) * Advertising cost ($)

A. Per Pay Click
B. Prize per click
C. Pay Per Click
D. Both A and B

A. Product ID
B. Product Name
C. Product Loading
D. None of above

A. TRUE
B. FALSE