We can define machine learning as the science of making computers learn to work as human beings by providing data and information without the proper explicit program and instructions. It has various techniques like Supervised machine learning, unsupervised learning, semi-supervised learning and, reinforcement learning. In the process of learning today, we will discuss Supervised machine learning Vs. Unsupervised learning.
Supervised Machine Learning Vs. Unsupervised Machine Learning
in our discussion will discuss the difference between supervised machine learning and unsupervised machine learning. Will try to tell clarify its types and real-life application in various sector.
Supervised Machine Learning
Supervised machine learning is under the supervision of some systems and processes. The learning process model can predict with the help of a labeled data set. To make you more clear, let me tell you something leveled data set. Data that is already known as the target answer is called the labeled dataset. For example, when I show you the cow’s image and tell you it is a cow, it is considered level data. On the other hand, when I show you any other image without saying anything, it is considered unleveled data.
How does it work?
You have seen in the example that when we order for any request, the machining process the data, train, analysis. After that, it makes a prediction and provides a decision based on the past.
Features of supervised learning
- Supervised learning provides output based on previous examples.
- This learning infers a function from the labeled training data.
- The desire output value is considered a supervisory signal.
- Machine learning algorithm analysis train data produced inferred function to map a new example.
- In the optimal scenario to determine class all levels for an unseen instance.
Supervised Learning Example
If you ask Siri, “Hey Siri, how far is the nearest petrol station?”. So whenever you ask Siri, it will take your voice as a text form like 01100. it will record with the help of machine learning and neural network and send it to the Apple server for further processing. Then new machine language processing algorithms will run to understand the intention of the user. And finally, I will send you the answer like “the nearest petrol station is 3 kilometers away”. This is the example of machine learning where which is working as a Human. Whether the feeding of data and information machine learning to give you the result.
Another Example of This Learning
When we input the system, the image of a pen and notebook. We will brief the details of this item to the computer, like the size, dimension, characteristics, weight, and many other features. It is known as the leveling of data. Now, if you’re so the image of a pen to the system, it will be easily recognizable than the image of a pen. This is known as supervised machine learning. The algorithm helps to learn the process based on the previous leveled data set and experience.
We can divide father supervised learning into classification and regression.
When the output variable is categorical, like two or more classes, we use classification. Here the answer is set like true/false and yes or no. The output comes based on the category like black or white, male or female, and fit or unfit.
The relationship between two or more variables associated with each other for changing the value of another variable. For example, when you ask for a salary, it depends on your working experience. The height weight chart according to age can be an example of regression machine learning.
Correlation Between Two
When we determine the employees will get a salary or not, it is a classification, but if we want to tell how much salary he should get, it depends on regression. We want to clarify more the classification based on some examples. If you want to predict an email is spam or not, we must teach the machine how the spam emails look like. Spam mail has several criteria like the content of the email, email header, and various information. It also has some tricky words like lottery claim, free offers, etc. It also recognizes the already listed spammers. So all those criteria score an email whether it is spam or not. If the score is low, then the machine learning sent the email to the inbox. Otherwise, it sends it to the spam box.
Now will learn the application of regression. We can set an example with two variables first one is humidity, and the second one is temperature. Here humidity is the dependent variable, and temperature is the independent variable. When the humidity increases, the temperature is decreased. The assumption is vice versa. When will fix the model and variable in the machine learning regression model. It will understand the relationship between the two variables and how one variable is dependent on another. Now the train part of machine learning is over. So if you give the input of humidity or temperature, it will show you the temperature or humidity.
Some Real-life Application of Supervised Machine Learning
super fast machine learning is used to assess the risk in the financial sector. To minimize the risk portfolio, the insurance company uses this machine learning method. Decide insurance companies much other banking and non-banking financial institutions are also using the model of supervised learning. In our previous article, we have discussed several machine learning languages for risk assessment.
All of us using social media like Facebook, Twitter, etc. Facebook recognizes the images of your friends despite not tagging any photo. Image classification is one of the classified machine learning examples to demonstrate the images of social media. Besides image recognization, any other application use this machine learning method like neural network and decision tree.
Whether the transaction is compensated by the actual authenticate user or not is verified by supervised machine learning. The financial institution allows biometric recognition, strong password systems, and a separate dongle device because of passport security.
The machine learning model’s ability to identify objects, images, size, nature, color, actions, and many other variables. to unlock your mobile phone on the laptop using your face recognition system is a suitable example of supervised machine learning.
Unsupervised Machine Learning
In unsupervised learning, the algorithm is trained using data that is unlevelled. There is no supervision and training given to the machine. It allows working with data that is not leveled. Hear machine tries to identify the pattern and give the expected outcome.
Example of Unsupervised Learning
In this example, we want to show a similar example as previously. At this time, we will not tell the machine about the features of a pen and notebook. The machine identifies to recognize the pattern of the previous set and recommend the solution. Quantum machine learning can be another example of unsupervised learning.
Again we can divide father the unsupervised machine learning into clustering and association.
When the machine from the data group is based on the behavior of data. It divides the object into plaster which is similar between them. With the dissimilar data, it makes another cluster.
Association is rules-based machine learning. It discovers an interesting relationship between variables from the large data set.
Correlation Between Two
This example is not like Supervised Machine Learning. Who is the customer who has a similar pattern of purchasing the products is the example of clustering. But the association is which products the customer purchase together.
I want to make clear clustering with an example. To reduce the churn rate, the telecommunication company studies the behavior of the customer. The internet uses an average call duration to find out the correlation between course duration and internet uses.
I want to make clear the example with a chat. In the chart, we can see that when the call duration is higher at point A, the internet uses higher. Points B and C show the adverse relationship with point A.
The telecom company offers the users to the individual customer based on their requirements by implementing this figure. When the users use more voice, they are offered various call plans on the other hand, when the user uses the internet more, they are provided the options for internet uses.
Example of Association Machine Learning
Customer Foss goes to the supermarket and purchases bread, milk, fruits, and wheat. Customer Guru goes to the supermarket and purchases bread, milk, rice, and butter. If the customer Hawlader comes to market and purchases bread, it is highly likely to purchase some milk. Here the relationship is established based on the behavior of the customer and recommendation.
Some Real-life Application of Unsupervised Learning
Market Basket Analysis
Market basket analysis is the machine learning model based on some algorithms. When you buy some items, it is less likely to buy another group of items.
Semantic Clustering shows similar words based on a similar context. People post or Search websites based on their requirements. The semantic Clustering groups all the clusters with the same meaning and slows you. So that the customer finds the information they want very easily and efficiently. It provides you good browsing experience. Which is the credit of the search engine and best Internet browser?
Delivery store optimization
Michelin learning models are used to predict the requirement of customers. When the demand is more it automatically optimize its system. According to the past data and behavior of the demand, it can set a new demand.
Identifying accident-prone areas
Based on the intensity of the accident machine learning model can identify the accident-prone areas. It helps the authority to make conscious of the drivers against the accident.
Details Study of Supervised Learning Vs. Unsupervised Learning
After a long discussion, we can easily point out the gist points into the table form to clarify the topic. It will answer all of your doubts.
|Supervised Machine Learning
|Unsupervised Machine Learning
|Supervised Machine Learning is the trained set of data and based on some previous input.
|Unsupervised Machine Learning is based on untrained data and some patterns without any specific input.
|It uses labeled and known data as input.
|It uses an unlabeled and unknown data set as input.
|There is a feedback mechanism in this model.
|This model does not allow any feedback mechanism.
|Decision tree, support vector machine, and logistic regression are examples of supervised Machine Learning.
|Hierarchical clustering, apriori algorithm, and k mean clustering are examples of Unsupervised machine learning.
Supervised Machine Learning and Unsupervised Learning are used for fulfilling the requirement of us. Each of them is valuable. The scientists are trying to use both together. Since the function and mechanism are different so we can not tell that which one is best. However, Supervised Machine Learning and Unsupervised Machine Learning are mostly desired.