Unsupervised Learning
Unsupervised Learning is a one of the types of machine learning . It's a part of learning where we don't offer focus to our model while preparing for example preparing model has just information parameter esteems. The model without anyone else needs to discover what direction it can realize. Unsupervised learning techniques don’t use a training set and find patterns or structure in the data by themselves. Clustering problems can be solved with an unsupervised approach.
Unlike supervised machine learning , unsupervised machine learning methods cannot be directly applied to a regression or a classification problem because you have no idea what the values for the output data might be, making it impossible for you to train the algorithm the way you normally would. Unsupervised learning can instead be used for discovering the underlying structure of the data.
Training data we are feeding is –
Unstructured data: May contain meaningless data, missing values or unknown data.
Unlabeled data : Data only contains value for input parameters, there is no output . It is easy to collect as compared to labeled one in supervised approach .
Types of Unsupervised Learning :-
Clustering: Broadly this technique is applied to group data based on different patterns, our machine model finds. For example in above figure we are not given output parameter value, so this technique will be used to group clients based on the input parameters provided by our data.
Association: This technique is a rule based ML technique which finds out some very useful relations between parameters of a large data set. For e.g. shopping stores use algorithms based on this technique to find out relationship between sale of one product w.r.t to others sale based on customer behavior. Once trained well, such models can be used to increase their sales by planning different offers.
The best time to use unsupervised machine learning is when you don’t have data on desired outcomes, like determining a target market for an entirely new product that your business has never sold before. However, if you are trying to get a better understanding of your existing consumer base, supervised learning is the optimal technique.
Some algorithms:
· K -Means Clustering
· DBSCAN – Density-Based Spatial Clustering of Applications with Noise
· BIRCH – Balanced Iterative Reducing and Clustering using Hierarchies
· Hierarchical Clustering
Learn More About Supervised and Unsupervised Learning in Machine Learning and find more in CODEC https://www.codecnetworks.com/
And Training https://www.codecnetworks.com/Trainings/Big-Data-Analytics/Machine-Learning.php
Author—Deepak Chyaunal
Mentor—Mr. Vishwaprabhakar
