Know The Key Differences Between Supervised & Unsupervised Learning

Pavithra S 4 years ago

While Artificial Intelligence (AI) has been growing rapidly in this technology-driven world, machine learning- a division of Artificial Intelligence is gaining prominence for research and development. Machine learning trains computers to carry out various tasks through different examples and life experiences. From self-driving cars to SIRI and many other applications of our daily life, we use machine learning algorithms. This machine learning comes in different flavors and its two main categories include Supervised and Unsupervised learning, which we are going to explore here.

What is Supervised Learning?

Supervised Learning is one of the most popular categories of machine learning algorithm that applies to situations where the outcome of input data is known. Artificial Intelligence algorithms require more human-labeled examples which the supervised learning helps in training the machine using a well-labeled data. This means some data is already designated with the appropriate answer and it helps in predicting the outcomes for unforeseen data. Technical expertise and highly skilled data scientists play significant roles in successfully building, scaling, and deploying precise supervised machine learning Data science model and it takes time.

For example, supervised learning helps determine the time taken to reach back home based on the weather condition, time of the day, holiday, and the route chosen.

Unsupervised Learning

Unsupervised Learning is another machine learning technique where the supervision of a model is not required. In contrary to supervised learning, unsupervised learning doesn’t require labeled data. Instead, it mainly deals with unlabelled data and observes through the training examples and divides them into groups based on their shared characteristics. Unsupervised learning allows the model to work on its own to find information. It is used to perform more complex processing tasks when compared to supervised learning. However, unsupervised learning is more unpredictable than other natural deep learning and reinforcement learning methods.

Example: Unsupervised learning helps a baby identify different dogs based on the past supervised learning.

The supervised and unsupervised machine learning techniques are used in various applications of AI such as data processing systems that contain a huge number of greatly interlinked processing elements.

Key differences between Supervised & Unsupervised Learning

Parameters	Supervised Learning	Unsupervised Learning
Base	It deals with labeled data	Manages unlabeled data
Process	Input and output variables are given	Only input data is given
Algorithms Used	Support vector machine, Linear and logistics regression, Neural network, random forest, and Classification trees.	Cluster algorithms, K-means, hierarchical clustering, etc.
Use of Data	Uses training data to establish a link between inputs and outputs	No output data is used
Sub-types	Classification and Regression	Clustering and Association Rule mining
Analyzation	Offline	Real-time
Computational Complexity	High and simpler method	Low and computationally complex
Results	Highly accurate and trustworthy	Less accurate, but trustworthy
No. Of Classes	Known	Not known
Drawback	Classifying big data can be challenging	No precise information regarding data sorting and the output

Why Supervised Learning & Unsupervised Learning?

Supervised Learning permits you to collect data or produce the output of data from the previous experience. It helps to streamline performance criteria using the experience gained already. The supervised learning also helps to solve various real-world computation problems.

The primary reasons for using unsupervised learning include

It helps find all kind of unknown data patterns and find features which would be useful for systemization. Since it takes place in real-time, all the input data is well analyzed and then labeled in the presence of learners. It makes it easier to get unlabeled data from a computer than the labeled data which requires manual intervention.

To summarize, supervised learning is the technique used in machine learning to accomplish a task by providing adequate training, input, and output patterns to the system. On the other hand, unsupervised learning is a self-learning technique in which the system has to identify the features of the input population by itself with no prior set of categories being used. Data Science is the highest demanding career today providing ample job opportunities for young minds. If you are interested in learning data science, connect with Sulekha where you will find a comprehensive list of institutes that offer various data science courses and business analytics training at reasonable fees.