Before moving to supervised and unsupervised learning, We shall start with the very basic definition of Machine learning first. Machine learning is defined as the branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable AI use human intelligence to learn and program. The concept of machine learning is pretty vast but in this article we would have a detailed discussion on two major machine learning types that are Supervised learning and unsupervised learning.
What is Supervised learning?
Supervised Machine learning is a type of learning that involves a supervisor as a teacher. This process involves teaching the machine to work with a set of data that is well-labeled, Which means some data is already stored with the correct answer. Later, the machine is fed with a new set of data so that the learning algorithm analyses the training data and produces an appropriate outcome from labeled data. For example, a labeled dataset of images of fruits say Apple, Mango, and Banana would have each image tagged with either “Apple”, “Mango” or “Banana.” This is further divided into two more categories, namely:
- Regression Supervised machine learning
- Classification-supervised machine learning
1. Regression Supervised Learning
Regression Supervised Machine learning is a technique that deals with individual data elements and thus predicts an output variable based on one or more labeled input variables. This type of supervised learning first establishes a relationship between the dependent variable and the independent variables. Common Regression Algorithms include-
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Decision Tree Regression etc.
Classification Supervised learning
Classification Supervised Machine learning predicts a categorical output variable based on the fed input variables rather than a single individual output. This involves categorization of data into predefined groups and a special function is used to map the input to a probability distribution over various output classes. Some common classification algorithms include:
- Logistic Regression
- Support Vector Machines
- Decision Trees etc.
Why Supervised machine learning?
Why do we use Supervised learning can be answered by the fact that it has a clear objective and allows us to collect data and produce output from previous experiences. These algorithms are very accurate and efficient. Moreover, these algorithms are very flexible in nature and can solve various types of real-world computation problems.
Not only this, but Supervised learning models can also automate various time-consuming tasks, such as sorting emails, diagnosing medical images, and detecting fraudulent transactions as we discussed before. The most important feature of this machine learning type includes a proper and efficient way of handling large datasets. Now, we must study the other side of the comparison.
What is Unsupervised learning?
Unsupervised Machine learning deals with unlabelled data. These algorithms are designed to discover hidden patterns or data groupings without any human intervention. This type of learning discovers similarities and differences in information and makes relevant output. It is used in several fields clustering, anomaly detection, etc. It is the training of a machine using data that is not labeled thus allowing the algorithm to predict without any prior guidance.
For Example: you have a large dataset of unlabeled images and the model has not been given any prior information about the features of these images. Thus, Unsupervised machine learning helps you to identify the patterns of the data elements and categorize all the elements based on the pattern of similarities and differences observed by the machine itself without any prior information. Now, we shall study about its various types that include:
- Clustering unsupervised machine learning
- Association unsupervised machine learning
1. Clustering Unsupervised Learning:
It is a technique in unsupervised machine learning that is used to group and form clusters of similar data points together based on their inherent characteristics. Briefly explained we make groups of similar data points together. This further has various types like:
- Hierarchical clustering
- K-means clustering
- Principal Component Analysis
- Singular Value Decomposition etc.
2. Association Unsupervised Learning:
This is a technique of unsupervised machine learning that attempts to find relationships or patterns among a set of items fed into a machine. We use algorithms to detect patterns in the data. Some common association rule learning algorithms include:
- Apriori Algorithm
- Eclat Algorithm
- FP-Growth Algorithm
Why Unsupervised Learning?
Unlike supervised learning, the unsupervised machine learning algorithms do not require training data to be labeled which helps you to decrease maximum human intervention as no prior information has to be entered in the system. These algorithms are capable of finding unknown patterns in data which can further help you to gain insights from unlabeled data.
These learning models are very adaptive in learning new trends and patterns of unknown data and have this high scalability when working with large sections of data. Adding on to the features this model is cost-efficient as they do not require labeled data. This significantly reduces the cost of human resources required for the same and the effort associated with data collection and labeling it as per the requirements of the machine.
Difference between Supervised and Unsupervised Learning
Now, as we have studied about both machine learning algorithms, we shall understand the difference between them. Both supervised and unsupervised learning methodologies have a lot of contrasting features which we will study in this section.
Supervised Learning
- Data: Uses labeled data.
- Goal: Learn a mapping from inputs to outputs.
- Types include- Regression, Classification etc.
Unsupervised Learning
- Data: Uses unlabeled data.
- Goal: Identify patterns, structures, or groupings in data.
- Types include: Clustering, Association, etc.
Above are some basic differences between supervised and unsupervised learning. However, the contrast is not limited to these figures only. As we have already studied before that in supervised learning we have a supervisor who classifies the training data sets and labels them for further computation, whereas in unsupervised learning no such classification is fed into the system by any supervisor, the identification is the part of the learning process. It is in the process, where the identification, classification and then organisation of these data sets takes place. We shall have a closer look on these factors individually:
- Input Data: Supervised learning makes use of labeled data which is fed into the system for further computation, all input sets have a guaranteed correct output set. Whereas if we talk about unsupervised learning, this primary works on unlabeled data i.e inputs data set doesn’t have any corresponding output.
- Identification of relationship: A relationship is established between the input and the corresponding output set in supervised learning algorithms on the other hand, in unsupervised learning a relationship is established between a single input and a wide range of possible outputs.
- Outcome: Supervised learning aims at predicting outcomes for new data taking insights from the already defined features whereas unsupervised learning deals with discovering hidden patterns in the data.
- Computational Complexities: Supervised learning algorithms are easier to implement and thus have less computational complexities. In the case of unsupervised learning algorithms, computational complexities are more , due to extensive algorithm roadmap.
- Feedback mechanism: We have a proper feedback mechanism in Supervised learning where feedback is taken in the form of correct answers at the time of training. No such thing is possible in the opponent type.
- Accuracy: Supervised machine learning algorithms produce accurate and reliable results as the data is already fed into the system, thus the possibility of getting errors is less in this case. However, there are certain possibilities of getting not-so-accurate results in the case of unsupervised learning as we deal with more complex data types here.
Learn Supervised And Unsupervised Learning With PW Skills
Start your exciting journey into the modern world of AI with our comprehensive PW Skills Generative AI course specially designed by experts to cater people with different skill sets.
The key features of this course that make it a stand-out choice among users include an in-demand syllabus, expert mentorship, beginner-friendly content, working on practical projects, daily doubt-clearing sessions, 100% placement assistance, networking opportunities, alumni support, resources to enhance learning, Easy EMI options on course fees, and much more.
Join today through PWskills.com and grab your seat in the demanding course.
Supervised And Unsupervised Learning FAQs
What are the other types of machine learning algorithms?
Besides supervised and unsupervised learning algorithms, we have several other types including:
• Semi-Supervised Learning
• Reinforcement Learning
• Self-Supervised Learning
• Transfer Learning
• Multitask Learning
• Active Learning
What is machine learning?
Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable AI use human intelligence to learn and program.
What are the disadvantages of supervised machine learning algorithms?
Supervised learning requires a large amount of labeled data i.e. we need to feed a lot of data into the machines before the entire process to get automated. Therefore, it can be quite time-consuming and expensive in many cases.
