Machine learning is a technique that allows computers to learn without the need for programming. It’s an area of AI (Artificial Intelligence) and computer science that focuses on using data and algorithms to mimic the way people learn intending to steadily improve accuracy.
Machine Learning vs. Deep Learning vs. Neural Networks
Because machine learning and deep learning are often used interchangeably, it is important to understand the difference. Artificial intelligence includes subfields such as machine learning, deep learning, and neural networks. On the other hand, deep learning is a branch of machine learning, while the neural network is a branch of deep learning.
Deep learning involves several layers of neural networks and makes extensive use of complex data sets. Neural networks are made of clusters that work together to produce the intended outcome. It mimics the neurons of the human brain.
Types of machine learning algorithms
There are three machine learning models: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning algorithms create a computational formula of a set of data that includes both inputs and intended outputs. The data is referred to as training data, and it consists of a collection of training instances.
Unsupervised learning algorithms take a collection of data with just inputs and detect structure in it, such as data point grouping or clustering. As a result, the algorithms learn from unlabeled, unclassified, and uncategorized test data.
Reinforcement learning is a type of machine learning that studies how intelligent agents should operate in a given environment to maximize the concept of cumulative reward.
To learn more about machine learning, see the blog posts below.