ai learning

Question 1

● Machine learning:The use of computers to identify patterns in data and make decisions based on this data

Question 2

● Deep Learning: A subset of machine learning that aims to create algorithms that mimic how the human brain works

Answer 2

by learning from incredibly large data sets

Question 3

● Supervised learning: The task of learning a function that maps an input to an output based on previous data sets with labelled input-output combinations.

Question 4

● Unsupervised learning: Algorithms that find patterns in unlabelled data where no previous patterns have been found. They do this by looking for connections or patterns in the data set.

Question 5

● Semi-Supervised learning: Algorithms that use a combination of labelled and unlabelled data

Answer 5

Used because labelling large data sets can be too expensive or time consuming. Uses the labelled data sets to “learn” how to treat unlabelled data

Question 6

● Reinforcement learning:algorithms that learn by interacting with the environment and using a system of “reward and punishment” to determine a best possible course of action This is the process used in the development of self driving cars

Question 7

● Data Quality: Poor data quality can lead to inaccurate outputs. When pre-processing data

Answer 7

you should aim to remove all missing values