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For example, classifying emails as spam or not spam uses a classification function to determine the appropriate
category for each email. Following are the uses of Supervised Learning:
Estimating Life
Expectancy Population Growth Identity Fraud Image
Prediction Detection Classification
Market
Forecasting
Weather
Forecasting Supervised
Regression Learning Classification
Advertising
Popularity
prediction
Customer Diagnostics
Retention
Advantages of supervised learning:
• With the help of supervised learning, the model can predict the output on the basis of prior experiences.
• In supervised learning, we can have an exact idea about the classes of objects.
• Supervised learning model helps us to solve various real-world problems such as fraud detection, spam filtering,
etc.
Disadvantages of supervised learning:
• Supervised learning models are not suitable for handling the complex tasks.
• Supervised learning cannot predict the correct output if the testing data is different from the training dataset.
• Training requires a lot of computational time.
• In supervised learning, we need enough knowledge about the classes of object.
Unsupervised Learning
As the name suggests, unsupervised learning is a machine learning technique in which models are not supervised
using training dataset. The machine learns through observation and finds patterns in data. The system will explore
the data and draw inferences from the data set to describe the hidden patterns in the unlabelled data. Unsupervised
machine learning algorithms are used when the information used to train is neither classified nor labelled.
For example, if somebody gives you a basket full of different fruits and asks you to separate them, you will probably
do it based on their colour, shape, and size, right? Unsupervised learning works in the same way. For example,
Raw Data is input Output
• Unknown Input Algorithm
• No Training Data Set
Interpretation Processing
Model Training Model is trained
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