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Unsupervised Learning
Unsupervised learning is like learning without a teacher. 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. As you can see in the image:
Raw Data is input Output
Algorithm
• Unknown Output
• No Training Data Set
Interpretation Processing
Model Training Model is trained
Unsupervised Learning Algorithm and Its Uses
There is one algorithm of unsupervised learning called clustering. Clustering is a machine learning approach where
the machine partitions the dataset into different clusters or categories based on machine generated algorithms. The
uses of clustering algorithm are:
Targeted
Marketing
Recommender Customer
Systems Segmentation
Unsupervised
Clustering Learning
Reinforcement learning
Reinforcement learning methods resemble how humans and animals learn. Consider the scenario of teaching new
tricks to your pet dog. As the dog doesn’t understand English or any other human language, we can’t tell it directly
what to do. Instead, we follow a different strategy:
• We imitate a situation, and the dog tries to respond in many different ways. If the dog’s response is the desired way,
we will give it food.
• Now whenever the dog is exposed to the same situation, it executes a similar action with even more enthusiasm in
expectation of getting more reward(food).
• The dog learns “what to do” from positive experiences.
• At the same time, the dog also learns what not to do when faced with negative experiences.
Reinforcement learning algorithms interact with the environment by producing actions and discovering errors or
rewards.
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