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In supervised learning, the algorithm learns from labelled data, where each training example is paired with a
corresponding target label. The goal is to learn a mapping from input variables to output labels. During training, the
algorithm adjusts its parameters to minimise the difference between predicted and actual labels.
Building, expanding, and successfully implementing accurate supervised machine learning models requires time and
technical expertise from a team of highly trained data scientists. In the real-world, supervised learning can be used for
risk assessment, image classification, fraud detection, spam filtering, etc. Take a look at the examples below:
Example 1:
It is a
INPUT RAW Supervisor mango !
DATA Training data set Desired Output OUTPUT
Algorithm Processing Model Trained
Model Training
INPUT
Step 1: You provide the system with images of mangoes and tag them as mangoes. This type of input is referred to
as labelled data.
Step 2: The model learns from the labelled data and next time you ask it to identify a mango, it can do it easily.
That’s exactly how supervised learning works.
Example 2:
Many voice assistants, including Apple's Siri and Amazon's Alexa, use supervised learning algorithms to process and
interpret spoken instructions. The algorithms are trained on a dataset of labelled speech data (transcribed speech and
text), which they then use to transcribe and interpret spoken commands.
Analog Audio Analog to Digital Audio Pattern Recognition
Conversion
Supervised Learning Algorithms and Their Use
Supervised learning involves two primary algorithms: regression and classification.
● Regression algorithms create a mapping function from the input data, allowing us to predict continuous
outcomes. They are used if there is a relationship between the input variables and the output variables. For
instance, predicting house prices based on features like size and location is a task for regression.
● Classification algorithms, on the other hand, involve creating a function that assigns data points to specific categories. They
are used when the output variable is categorical, implying there are two classes such as Yes-No, Male-Female, True-False, etc.
Machine Learning Algorithms 323

