What is Machine Learning?
TechTarget defines machine learning as “… a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. It focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension—as is the case in data mining applications—machine learning uses that data to improve the program's own understanding. Machine learning programs detect patterns in data and adjust program actions accordingly.”
At Teradata, machine learning is considered especially powerful in a big data context, because machines can test hypotheses using large data volumes, refine business rules as conditions change, and identify anomalies and outliers quickly and accurately.
A machine learning based software system is trained using large data volumes and learns to act based on experience, making machine learning superior in problem-solving.
What is machine learning used for?
Image Recognition: One of the most significant machine learning applications, image recognition is a way to identify and detect features or objects in a digital image. This same technique can be used for a number of additional scenarios including pattern recognition, face detection, face recognition, and optical character recognition. Using machine learning in image recognition involves pulling key features from an image and transferring those key features into a reliable machine learning model.
Data Retrieval: The process of pulling knowledge or structured data from the unstructured data, known as data retrieval, is another significant use of machine learning due to the vast amounts of data created by the many devices in use. When it comes to big data, machine learning is critical for taking unstructured data and extracting the insights it contains.
Sentiment Analysis: The sentiment analysis process, sometimes referred to as opinion mining or sentiment classification, determines attitudes of individuals based on emotional clues within their writing. The goal of sentiment analysis is to determine what people think, whether good, bad, or indifferent. Review web sites and decision-making apps also benefit from sentiment analysis. Machine learning consists of supervised and unsupervised learning algorithms, both of which are used for sentiment analysis.
Fraud Detection: Fraud detection, specifically online fraud detection, is a more advanced application of machine learning that efficiently provides user cybersecurity, and even provides a way for businesses to reduce loss and maximize profit. The use of machine learning for fraud detection is vastly superior to traditional fraud detection methods.
Customer Shopping Recommendations: Your favorite online shopping sites can make such compelling offers to you—whether products, services, or special offers—because of machine learning. Machine learning methods such as supervised, semi-supervised, unsupervised, reinforcement are integral to recommendation-based systems.
Are there different types of machine learning?
There are some variations of how to define the types of Machine Learning Algorithms, but commonly they can be divided into categories according to their purpose. The main categories are:
Supervised Learning: The model is trained on a labelled dataset with both input and output parameters. Both training and validation datasets are labelled.
Semi-supervised Learning: Makes use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.
Unsupervised Learning: Also known as self-organization, unsupervised learning is used to find previously unknown patterns in a data set without pre-existing labels and allows modeling probability densities of given inputs.
Reinforcement Learning: Addresses how software agents should take actions in an environment to maximize some notion of cumulative reward. Unlike supervised learning, labelled input/output pairs aren’t required, and sub-optimal actions don’t require explicit correction. The focus is on striking a balance between exploration and exploitation.
What’s the difference between machine learning and deep learning?
A few basic differences between machine learning and deep learning are:
How They Work
Machine learning uses automated algorithms that learn to predict future decisions and model functions using the data it’s fed.
Deep learning interprets data features and relationships using neural networks that pass relevant information through multiple stages of data processing.
In machine learning, algorithms are directed by analysts to examine different dataset variables.
With deep learning, algorithms are typically self-directed for relevant data analysis.
Data Point Volume
Machine learning uses a few thousand data points for analysis.
Deep learning taps into a few million data for analysis.
A machine learning output is usually numerical, such as a score or classification.
A deep learning output can vary from a score, element, text, sound or other identifier.
What’s the difference between machine learning and artificial intelligence?
What They Are
Machine learning (ML) is the acquisition of knowledge or skill.
Artificial intelligence (AI) is the ability to acquire and apply knowledge.
The Purpose of Each
AI is focused on success, not accuracy.
ML is focused on accuracy, not success.
How They Work
AI works as a "smart" computer program.
ML is a simple machine that ingests and learns from data.
The Goal of Each
AI works to solve complex problems by simulating natural intelligence.
ML is task focused, working to maximize machine performance of the designated task.
What They Do
AI makes decisions based on data.
ML is a system that learns from data ingested.
What They Create
AI develops a system that mimics human responses and behavior under specific circumstances.
ML produces self-learning algorithms.
The End Product of Each
AI produces intelligence (business, consumer, market, etc.).
ML produces knowledge that can be further examined.