Supervised learning vs Unsupervised learning

Supervised learning vs Unsupervised learning

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Machine Learning is a crucial part of today’s business world, where technological integration plays a vital role in performing different business functions. Accordingly, Machine Learning allows computers to learn and act like humans by providing data. Apparently, ML algorithms ensure to train of the data enabling the new data input to make compelling predictions and deliver accurate results. Significantly, one of the most prominent examples of a Machine learning model is Siri by Apple, which can recognise speech converting audio into textual form. Therefore, Supervised Learning vs Unsupervised Learning is part of Machine Learning. Evidently, these include different tasks that help the former predict accurate results and identify underlying patterns. Let’s learn more about supervised and Unsupervised Learning and evaluate their differences. 

What is Supervised Learning? 

Supervised Learning is the type of Machine Learning where the training of an algorithm takes place using labelled data. Accordingly, these data in a dataset are designed for training or supervising algorithms that help predict the outcomes accurately. Effectively, with the help of labelled inputs and outputs, the machines can learn the process over time and measure the results. Significantly, Supervised Learning is practical in two types of tasks-

  • Classification: the goal is to predict a categorical label for each input data point
  • Regression: the goal is to predict a continuous value.

Accordingly, Examples of Supervised learning include linear regression, logistic regression, decision trees, random forests and neural networks. 

Applications of Supervised Learning 

Supervised learning has various applications across various industries. Accordingly, some typical applications of supervised learning include

  • Image and object recognition: Supervised Learning Algorithms are helpful in accurately classifying and recognising objects in images. Accordingly, it is useful in autonomous vehicles, surveillance, and healthcare.
  • Speech recognition: Effectively, Supervised Learning Algorithms help to transcribe speech to text, which uses voice-activated assistants and automated transcription services.
  • Fraud detection: Moreover, using Supervised Learning Algorithms enables the detection of fraudulent transactions and activities, which is helpful in finance and banking.
  • Spam detection: Furthermore, applications of Supervised Learning Algorithms help filter spam messages from legitimate ones. Accordingly, it is useful in email services and social media platforms.

Supervised learning vs Unsupervised learning

What is Unsupervised Learning? 

Unsupervised learning is a type of Machine Learning in which an algorithm is trained using data that does not have pre-existing target labels. Accordingly, Unsupervised Learning aims to identify patterns, structures, and relationships within the data without being given specific output labels to predict. Furthermore, in unsupervised learning, the algorithm is given a dataset with only input features and is asked to discover underlying patterns and relationships in the data. Significantly, there are two types of Unsupervised Learning:

  • Clustering: which involves grouping similar data points together. 
  • Association: a technique to discover relationships between variables in a dataset.

Effectively, some instances of Unsupervised Learning Algorithms include k-means clustering, hierarchical clustering, principal component analysis (PCA), and association rule learning. 

Applications of Unsupervised Learning 

Unsupervised learning has several applications across various industries. Accordingly, some typical applications of unsupervised learning include: 

  • Clustering: Unsupervised Learning Algorithms can be used to group similar data points based on their characteristics, which is helpful in market segmentation, customer profiling, and image segmentation.
  • Anomaly detection: Furthermore, Unsupervised Learning Algorithms can identify unusual data points or patterns that do not fit the expected behaviour, which is helpful in fraud detection, network intrusion detection, and fault detection in industrial processes.
  • Dimensionality reduction: Moreover, Unsupervised Learning Algorithms can reduce the number of features in a dataset while retaining the essential information, which is helpful in data compression, visualisation, and training in Supervised Learning Algorithms.
  • Association rule: Additionally, Unsupervised Learning Algorithms can identify hidden relationships between variables in a dataset, which is helpful in market basket analysis, customer behaviour analysis, and recommendation systems.

Differences between Supervised Learning & Unsupervised Learning 

Supervised Learning Unsupervised Learning 
There are two different types of Supervised Learning, regression and classification.  There are two types, including clustering and Association. 
Both input and output data are given to the model in this type of Machine Learning  However, Unsupervised learning only provides input data. 
Additionally, Supervised learning predicts the output.  On the other hand, unsupervised learning finding hidden patterns in the data 
The use of labelled data helps train run learning algorithms.  Effectively, the use of unlabelled data helps in preparing Unsupervised Learning Algorithms. 
Moreover, a supervised learning model helps in producing accurate results.  Significantly an unsupervised learning model Needs to have more accurate results. 
Additionally, the primary objective of Supervised learning is to train the model to predict by the input of new data.  Evidently, the primary aim of unsupervised learning is to find valuable insights and hidden patterns from unknown datasets. 
It includes various algorithms like linear regression, logistic regression, decision trees, bayesian logic, etc.  Accordingly, unsupervised learning includes algorithms like KNN, clustering, and Apriori Algorithm. 
Effectively, Supervised learning accepts Direct Feedback for assessing that output prediction is accurate. Comparatively, no input is taken by unsupervised learning. 
Supervised learning requires training for each data model and does not resemble Artificial Intelligence.  Evidently, unsupervised learning reaches AI as it keeps learning new things with every unique experience. 
Considerably, Supervised learning knows the number of classes.  In contrast, Unsupervised learning does not know the number of types. 
Moreover, the computational complexity is high in this case.  In comparison, unsupervised learning, computational complexity is far less. 
Significantly, Supervised Learning uses offline analysis.  Evidently, Unsupervised learning, in contrast, uses real-time data analysis. 
Evidently, Supervised Learning applications include image and object recognition, speech recognition, pattern recognition, etc.  Moreover, Unsupervised Learning applications include anomaly detection, clustering, dimensionality reduction, etc. 


As the blog concludes, you know the differences between supervised and unsupervised learning. Both these learning methods are part of Machine Learning which helps you build models using different algorithms and remain crucial for businesses. Accordingly, to learn more about Machine Learning, join Pickl.AI’s Data Science course for Working Professionals or Data Science Job Guarantee Program. Effectively, these courses provide you with extensive and industry-relevant modules, of which Machine Learning is a huge part. Consequently, Supervised and Unsupervised Learning is part of these courses that enable you to become proficient in building ML models. However, these courses can help you choose a future career path as a Machine Learning Engineer. 


  • Asmita Kar

    Written by:

    I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.