Supervised and Unsupervised Learning – Explained Through Real World Examples

Introduction

We live in the world of people and machines. Humans have learned and evolved from billions of years of past experiences, but the era of machines and robots is still in its infancy. In today’s world, these machines or robots need to be instructed to work, but what happens when the machine starts learning on its own? This is where machine learning comes in handy. Machine learning consists of applying mathematical and statistical approaches to get machines to learn from data. It includes many techniques but here we will only discuss two of them:

• Supervised machine learning

• Unsupervised machine learning

In this article, we’ll explore the purpose of machine learning and when we should use specific techniques. Consequently, we’ll find out how they work based on challenges solved through Omdena. You will have a clear picture of supervised and unsupervised learning after going through these examples.

Supervised machine learning

What is supervised machine learning?

Focusing on our own history, we observe that we human beings are not born with ready skills, so we need to first learn things to make our life easier like “how to sort mails, land aeroplanes, and have friendly conversations”. In the same way, computer scientists have tried to help computers to learn like we do, with a process called supervised machine learning. 

Supervised Machine learning is a method of inputting labelled data into a machine learning model. The model is trained with known input and output data so that it can predict future outputs accordingly. Additionally, you often need to prepare your data to improve its quality, fill the gap, and optimize it for training.

Types of supervised learning algorithms

There are various types of Supervised Machine Learning Algorithms such as 

• Linear Regression, 
• Logistic Regression, 
• Support Vector Machine (SVM), 
• Multi-class Classification, and 
• Decision tree. 

Let’s see the advantages and disadvantages of supervised learning.

Advantages & disadvantages of supervised learning

Advantages:

• Supervised learning permits you to be unmistakable with regards to the meaning of the marks. All in all, you can prepare the calculation to recognize various classes where you can define an ideal choice limit.­­­­­­­
• You can decide the number of classes you need to have.
• The information in supervised learning is very notable and is marked.
• The outcomes delivered by the directed strategy are more precise and dependable in contrast with the outcomes created by the solo procedures of AI. This is essentially on the grounds that the information in the regulated calculation is notable and marked. This is a critical distinction between administered and solo learning.
• The responses in the examination and the result of your calculation are probably going to be known because the relative multitude of classes utilised is known. 

Disadvantages:

• Supervised machine learning can be a complicated strategy in examination with the solo technique. The key explanation is that you need to see well overall and mark the contributions to manage learning.
• It doesn’t happen continuously while solo learning is ongoing. This is additionally a significant contrast between supervised and unsupervised learning. 
• It requires a ton of calculation time for preparing.
• Assuming you have dynamic, large, and developing information, you don’t know of the marks to predefine the standards. This can be a genuine test. 

Furthermore, the advantages as well as disadvantages of managed AI exceptionally rely upon what precisely administered realising calculation you use.

What is supervised machine learning with examples?

Omdena has been providing real-world solutions by building different projects. One of the Supervised Machine Learning examples is Smart Data Labelling with ML. Supervised machine learning tasks require a large amount of data to be acquired in order to build complex models and improve predictive power. However, desirable results cannot be achieved without objectively characterising the available data. 

The Active Learning: Smart Data Labelling with ML (Machine Learning) article describes the intuition and implementation of a supervised learning model in combination with an active learning algorithm for labelling data. Active learning leverages both manual and automatic labelling to optimise the labelling process.

In this project two approaches to labelling are done: manual labelling and automatic labelling.

Read the whole article written by Tan Jamie: Active Learning: Smart Data Labelling with ML (Machine Learning)

A few more instances of machine learning applications include:

• In money and banking for charge card extortion location (misrepresentation, not misrepresentation). Indeed, regulated learning gives probably the best inconsistency discovery calculations. Check Omdena case study Machine Learning for Credit Scoring: Banking the unbanked
• Anomaly detection & identification. Check Omdena’s case study Anomaly Detection on Mars Using Deep Learning
• In the marketing area – a scope of text mining algorithm is utilized for message text sentiments analysis  (cheerful, not blissful). Check Omdena’s case study Understanding Youth Sentiments Through Artificial Intelligence
• In medication, for anticipating patient danger (like the high-hazard patient, okay persistent) or for foreseeing the likelihood of a congestive cardiovascular breakdown. Check Omdena’s case study A Mental Health Predictor using Artificial Intelligence
• In public safety, for building a tool for analysing and classifying cases of sexual abuse in the workplace to identify patterns of such behaviors. Check Omdena’s case study Analysing Sexual Abuse at the Workplace Using Supervised Learning

Unsupervised machine learning

What is unsupervised machine learning?

Unsupervised learning is an AI method wherein we don’t have to direct the model. It permits the model to chip away at its own to find examples and data that was beforehand undetected. It predominantly manages the unlabeled information.

Unsupervised learning algorithms allow to perform more mind-boggling handling errands contrasted with machine learning. Although Unsupervised learning can be more eccentric contrasted with other regular learning techniques. Unsupervised learning algorithms include anomaly detection, clustering, neural networks, etc.

Types of unsupervised learning algorithms

The type of unsupervised learning algorithms include:

• Hierarchical clustering.
• K-means clustering.
• K-NN (k nearest neighbours).
• Principal Component Analysis.
• Singular Value Decomposition.
• Independent Component Analysis.

Advantages & disadvantages of unsupervised learning

Advantages:

• Less intricacy in correlation with administered learning. In unsupervised learning, nobody is needed to comprehend and afterward name the information inputs. This makes solo learning less mind boggling and clarifies why many individuals favour unsupervised learning.
• It is frequently simpler to get unlabelled information – from a PC than marked information, which needs private mediation. This is likewise a critical distinction among managed and solo learning.

Disadvantages:

• You can’t get unmistakable with regards to the meaning of the information arranging and the result. This is on the grounds that the information utilized in solo learning is marked and not known. It is the occupation of the machine to name and gather the crude information prior to deciding the secret examples.
• Less exactness of the outcomes. This is additionally because the info information isn’t known and not marked by individuals ahead of time, and that implies that the machine should do this by itself.
• The consequences of the investigation can’t be found out. There is no earlier information in the unaided technique for AI. Moreover, the quantities of classes are likewise not known. It prompts the powerlessness to discover the outcomes created by the examination.

What is Unsupervised machine learning with examples?

Here is an example of a real-world problem solved using unsupervised learning on satellite images to identify climate anomalies. 

Somalia is a small country in the continent of Africa. The country exhibits a lot of natural disasters and terrorism as a result of which people of Somalia go through mass displacements leading towards a situation of lack of food and shelter. This article shows how to build an anomaly detection system using Machine Learning. The system is capable of capturing sudden vegetation changes, which can be used as an alert mechanism to provide immediate relief to the people and communities in need.

Read the whole article written by Animesh Seemendra: Using Unsupervised Learning on Satellite Images to Identify Climate Anomalies

What is the difference between supervised and unsupervised learning techniques?

The main & classical difference between both learning is:

• Supervised learning = uses labelled data

• Unsupervised learning = uses unlabeled data

Well the main difference between supervised and unsupervised learning is that supervised learning uses off-line analysis whereas unsupervised learning uses real-time analysis of data. In supervised learning the number of classes is known but in unsupervised learning the number of classes is unknown. The results of supervised learning are accurate and reliable, on the other hand, the results of unsupervised learning are moderate, accurate, and reliable. 

Conclusion

Anyway, which is better, supervised learning or unsupervised learning? According to the advantages & disadvantages stated above, we can’t say that only supervised machine learning or unsupervised machine learning is better. It totally depends on the case or problem you’re facing then according to the problem we apply any of the learning. In view of this current, it’s wrong to say that unaided and managed strategies are options in contrast to one another. The essential assignments and issues you can resolve with administered and solo strategies are unique. When to utilize either strategy, it relies upon your necessities and the issues you need to tackle.