Supervised vs Unsupervised Machine Learning: What's the Difference?
Machine learning is a powerful tool that has revolutionized many industries. One of the main goals of machine learning is to teach computers how to learn patterns from data. There are two main types of machine learning: supervised and unsupervised.
Supervised Machine Learning
Supervised machine learning is a type of machine learning where the computer is given a labeled dataset to learn from. In other words, the computer is provided with input data (also called features) and corresponding output data (also called labels or targets) to learn from. The goal of supervised learning is to learn a mapping function that can accurately predict the output for new input data.
For example, let's say you want to build a machine learning model to predict the price of a house based on its size, location, and number of bedrooms. You would start by collecting a dataset of houses that includes their sizes, locations, number of bedrooms, and their corresponding prices. This dataset is the labeled dataset that you would use to train your supervised learning model.
The supervised learning algorithm would take this labeled dataset as input and learn a mapping function between the input features (size, location, and number of bedrooms) and the output label (price). Once the model is trained, you can use it to predict the price of a new house based on its size, location, and number of bedrooms.
Some popular examples of supervised learning algorithms include linear regression, decision trees, random forests, and neural networks.
Unsupervised Machine Learning
Unsupervised machine learning, on the other hand, is a type of machine learning where the computer is given an unlabeled dataset to learn from. In other words, the computer is provided with input data but without corresponding output data. The goal of unsupervised learning is to find patterns and structure in the input data.
For example, let's say you want to analyze customer data to identify different groups of customers based on their purchasing behavior. You would start by collecting a dataset of customer transactions that includes information such as purchase date, item purchased, and price. This dataset is the unlabeled dataset that you would use to train your unsupervised learning model.
The unsupervised learning algorithm would take this unlabeled dataset as input and analyze the patterns and structure in the data to identify different groups of customers based on their purchasing behavior. Once the model is trained, you can use it to segment your customers into different groups based on their purchasing behavior.
Some popular examples of unsupervised learning algorithms include clustering, principal component analysis (PCA), and autoencoders.
Conclusion
In summary, supervised and unsupervised machine learning are two main types of machine learning. Supervised learning involves learning from labeled data, while unsupervised learning involves learning from unlabeled data. Both types of machine learning have their own unique applications and use cases, and choosing the right type of machine learning algorithm depends on the specific problem you are trying to solve.