16 Questions
What is machine learning?
Machine learning is a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way humans learn, gradually improving accuracy.
What is a feature in the context of machine learning?
A feature is a measurable property or parameter of the data-set.
What is the role of a machine learning model?
A machine learning model is the mathematical representation of a real-world process, also known as 'hypothesis'.
How does the accuracy of predicted output in machine learning depend on the amount of data?
The accuracy of predicted output depends on the amount of data; a huge amount of data helps to build a better model which predicts the output more accurately.
What is the main purpose of using unsupervised learning?
To find useful insights from unlabelled and uncategorized data.
How does unsupervised learning resemble human learning?
It is similar to how humans learn from their own experiences.
Why is unsupervised learning important in real-world scenarios?
Because input data does not always come with corresponding output.
What is the primary method used in unsupervised learning to group objects with similarities?
Clustering
What is positive reinforcement learning?
Adding something to increase the tendency that expected behaviour would occur again.
What is a potential consequence of too much positive reinforcement?
An overload of states that can reduce the consequences.
How does negative reinforcement learning differ from positive reinforcement learning?
It increases the tendency of specific behavior by avoiding negative conditions.
What are the three traits that define a well-posed learning problem?
Task, Performance Measure, Experience
What is the incident called when a machine learning model continues to show the same recommendations to the customer despite the change in customer requirements?
Data Drift
What is the term for the situation when certain elements of the dataset are heavily weighted or given more importance than others, leading to inaccurate results?
Data Biasing
What type of data consists of numerical values and anything that is measured by numbers?
Quantitative Data Types
How can the challenge of bad recommendations and data drift in machine learning models be overcome?
By regularly updating and monitoring data according to the expectations.
Learn about the concepts of positive and negative reinforcement in the context of reinforcement learning. Understand how these types of reinforcement impact the behavior of the agent and the consequences of their application.
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