What is Unsupervised Learning ? | Unsupervised Learning Algorithms| Machine Learning | Edureka

Key Takeaways

foreign [Music] I welcome you all in today's session about what is unsupervised learning in today's session let's take a quick dive into some real-time Concepts about unsupervised learning but before we go ahead if you haven't already make sure to subscribe to the edurika's YouTube channel to never miss out on any updates from us also if you're looking for any of the edurical certification courses do check out the link given in the description below let's get ahead guys so firstly look at our agenda for today firstly we see what is unsupervised learning then we learn about why to use unsupervised learning followed by types of unsupervised learning and then we dive into how does unsupervised learning work and finally we talk about some of the advantages and disadvantages of unsupervised learning so guys without any Ado let's get ahead so firstly let's talk about what exactly is unsupervised learning so guys you should note that the goal of unsupervised learning which is a machine learning theory is to have algorithm to discover patterns structures or relationships within a data set with without any human supervision or label instances unsupervised learning makes use of data that has not been structured in any way also unsupervised learning seeks to identify hidden but present data structures such as groups clusters or patterns that could otherwise go unnoticed next let's talk about why to use unsupervised learning so the capacity of unsupervised learning is to uncover pattern structure like discuss so it can be useful for a broad variety of purposes and their implementation so guys here are many of the most important benefits of unsupervised learning first in the list we have outlier and anomaly detection here it is about a task that can be accomplished with unsupervised learning so Guys these are out of the ordinary occurrences which should be looked into further since they could be signs of error fraud or anything else so this comes under outlier and anomaly detection next we have clustering algorithms which can assist you to find groups of similar data points this can be beneficial for customer segmentation product grouping or even document categorization so guys this data can be used to improve marketing and recommended systems next we have in the list is image and text analysis these can be the common applications of unsupervised learning here the learning makes use of dimensionality reduction techniques for applications of image analysis for instance which can help in the visualization of high dimensional image data and also clustering in text analysis by grouping comparable articles for topic modeling next let's talk about data privacy and anonymization so guys in unsupervised learning it can be used to must personally identifiable information from data while still preserving the underlying structure and patterns this is the use for data privacy so guys unsupervised learning can also be used for dimensionality reduction which are useful in streamlining complicated data sets without sacrificing useful information through feature engineering and reduction techniques so guys this learning structure has the potential to boost model efficiency and speed up for future processing stages so guys now that we are familiar of why to use unsupervised learning let's move ahead with the types of unsupervised learning so guys here we have two classification which is the clustering and Association so firstly let's talk about clustering which is a form of unsupervised learning and a process of identifying and grouping the data points with shared properties so guys all the patterns with shared properties are identified in this form so guys here the objective is to discover clusters and grouping in the data that can occur naturally or without the use of arbitrary labels here the motive is to find hidden patterns or structures in which your data can be a common use case for clustering let's take for example a data sets which has the consumer in-store actions so cluster analysis can be used to group clients into subsets with similar buying habits for instance customers who tend to buy one type of goods more often than others might form one cluster while those who tend to buy a wide variety of things might form another so guys this is cluster analysis that allows you to find unique subsets without having to label them in advance now let's talk about Association so in unsupervised learning association means it is a process of discovering relationship between variables in a data set without having to access to the labels so guys when the labels are known and supervised learning the objective was to train a model to correctly predict the labels for fresh data so guys let's say you have information about grocery Shoppers habits so guys here you find patterns like customer who buy bread also often buy milk this could be discovered by association rule mining so that's the application of it these connections are useful as they can shed light on consumer habits and also inform advertising decision such as the placement of similar products in close proximity to one another and to promote cross-selling now that we took a good look about clustering and Association let's move ahead with how does unsupervised learning work so guys for this learning to be effective the data being studied must be completely unlabeled and unclassified for this to work so large amounts of data are needed for unsupervised machine learning so what data scientists do is they start the process by training algorithms on training data sets these data sets do not have properly labeled and categorize data points so the learning objective of the method is to discover patterns in the data set and classify the points according to those patterns so guys unsupervised Learning System may be trained to recognize characteristic traits like viscous long tails Etc by analyzing the photo of the cats for sample without the requirement for labels unsupervised learning improves corporate insights and decision making related to the customer Behavior next let's move on to the advantages of unsupervised learning so here first Advantage is that there are no labels required so since unsupervised learning doesn't require any label data it can be applied to a wide variety of data sets for which getting labels would be inconvenient and comparatively expensive next in terms of exploring the data unsupervised learning can unearth structures and patterns in the data that could otherwise be concealed from human eyes next we have in terms of future Discovery so by using unsupervised methods we may better understand that the data and the relevant features will improve the accuracy and efficiency of the subsequent machine learning models moving on we have anomaly detection which can be useful for spotting fraud errors and out of the ordinary Behavior so anomaly detection seeks out to figure and highlight these outlying data points lastly in terms of reduced biases so unsupervised learning can help estimate the possible biases which can be caused by human assessments because it does not rely on human label data so lastly moving on with the problems of unsupervised learning so firstly we have lack of ground truth so guys since unsupervised learning does not require labeled data there is no absolute standard by which to judge the quality of the model's protection this can make it hard to evaluate the effectiveness of the acquired clusters and patterns next we have is the challenges with subjective interpretation so guys unsupervised learning algorithm discovers patterns or clusters that may lend themselves to easy objective interpretation here the context an individual's perspective might play a role of determining the meaning of these patterns lastly what we have is overfitting so guys unsupervised learning lacks supervision of the label data that may produce patterns or clusters that are not transferable to unseen data overfitting can occur when a model incorrectly collects data because of its specificity so Guys these were quite a few illustrations about unsupervised learning I hope you enjoyed the video and if you did do hit the like button and stay notified for our further updates do click the Bell icon like always wish you happy learning I hope you have enjoyed listening to this video please be kind enough to like it and you can comment any of your doubts and queries and we will reply them at the earliest do look out for more videos in our playlist And subscribe to edureka channel to learn more happy learning