This is our second post in this sub series “Machine Learning Types”. Our master series for this sub series is “Machine Learning Explained”.
Unsupervised Learning; is one of three types of machine learning i.e. Supervised Machine Learning, Unsupervised Machine Learning and Reinforcement Learning. This post is limited to Unsupervised Machine Learning to explorer its details.
Unsupervised Machine Learning
In Unsupervised Learning available data have no target attribute. Machine Learning Algorithm takes training examples as the set of attributes/features alone. The purpose of unsupervised learning is to attempt to find natural partitions in the training set. The most common unsupervised learning method is cluster analysis at the same time two general strategies in UML includes:
System does self-discovery of patterns, regularities and features etc. from the input data and relations for the input data over output data. Discovering similarities and dissimilarities to forms clusters i.e. self-discovery is main target here. Since the examples given to the learner are unlabelled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.
Unsupervised learning – Pros & Cons
Since no labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a challenging goal in itself. The training data consists of a set of input vectors x without any corresponding target values; hence known as learning / working without a supervisor.
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