Introduction
People often use the terms Artificial Intelligence and Machine Learning interchangeably, but they do have some differences which we will see in this article. Also in this article we will explore terms like supervised learning, unsupervised learning, deep learning, etc.
Artificial Intelligence (AI) and Machine Learning (ML)
- Artificial intelligence, or AI, is an umbrella term that includes anything related to computers mimicking human intelligence. Some examples of AI applications include robots and self-driving cars.
- Machine learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed. This is in contrast to traditional programming, where the computer is told explicitly what to do.
- Machine learning mainly includes supervised and unsupervised learning. You might also hear the terms deep learning, or deep neural networks. This is a subset of machine learning that adds layers in between input data and output results to make a machine learn at much depth.
Supervised Learning
- For example, you are asked to classify dogs and cats from a very large set of pictures. You already know the difference between dogs and cats. So you label each picture and pass the labeled pictures to a machine. By learning from the data, in this case, pictures with the answers or labels, supervised learning is being enacted, allowing the machine to tell if a new picture represents a dog or cat in the future.
- Put simply, supervised learning deals with labeled data, is task-driven, and identifies a goal.
- Supervised learning provides each data point with a label, or an answer.
- There are two major types of supervised learning: The first is classification, which predicts a categorical variable, such as determining whether a picture shows a cat or a dog. In ML, you use models like a logistic regression model to solve classification problems.
- The second type of supervised learning is regression, which predicts a numeric variable, like forecasting sales for a product based on its past sales. You can use ML models like a linear regression model to solve regression problems.
Unsupervised Learning
- For example, you are asked to classify breeds of dogs from a very large set of pictures. Unfortunately, you don’t know many of them and are not able to label the pictures. So you send these unlabeled pictures to a machine. In this case, the machine learns from the data without the answers and finds underlying patterns to group the animals. This is an example of unsupervised learning.
- Put simply, unsupervised learning deals with unlabeled data, is data-driven, and identifies a pattern.
- Unsupervised learning does not provides data point with a label, or an answer.
- There are three major types of unsupervised learning: The first is clustering, which groups together data points with similar characteristics and assigns them to “clusters,” like using customer demographics to determine customer segmentation. You can use ML models like k-means clustering to solve clustering problems.
- The second type is association, which identifies underlying relationships, like a correlation between two products to place them closer together in a grocery store for a promotion. You can use association rule learning techniques and algorithms like Apriori to solve association problems.
- And the third type of unsupervised learning is dimensionality reduction, which reduces the number of dimensions, or features, in a dataset to improve the efficiency of a model. For example, combining customer characteristics like age, driving violation history, or car type, to create a simplified rule for calculating an insurance quote. You can use ML techniques like principal component analysis to solve these problems.