Although machine learning (ML) has been around for decades, its practical applications are now coming into focus as it helps companies better understand their customers. Available data from sources such as social media, mobile devices, and Internet of Things (IoT) devices is growing rapidly—we’re now generating an estimated 2.5 quintillion bytes of data every day. This flood of information has made machine learning more accessible than ever before.
To leverage the full potential of machine learning, however, it’s important to understand what it is, how it works, why it’s important, and the applicable use cases for your business.
Table of Contents
What is machine learning?
Machine learning is a subset of artificial intelligence (AI) that allow systems to learn and improve from experience without being explicitly programmed. It involves algorithms that make dynamic decisions and predictions based on historical data rather than following static program instructions for specific tasks and outcomes.
These decision-making programs are trained using large amounts of data. Once they’ve been fully trained, the programs use what they’ve learned to make accurate predictions or classifications in new situations.
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How does machine learning work?
ML algorithms perform several tasks with any given data, including time series analysis and visualizing past trends to determine what patterns might appear in future datasets. They can also predict probabilities based on previous data. However, effective machine learning algorithms require enough testing and tweaking until they produce consistent results for different use cases.
ML can provide value through predictive analytics and insights into customer behavior for businesses. It can also help automate tedious tasks like image recognition or even offer important support for decision-making processes.
Read more: Is Machine Learning the Same as Predictive Analytics?
Why is machine learning important?
Machine learning has become an integral part of how businesses run and make decisions. With the rise of new data-centric technologies, ML models can be leveraged for various critical business functions, such as fraud detection, content and experience personalization, search ranking, etc.
Machine learning is important because it gives enterprises a way to automate decision-making and action. This allows businesses to improve response times, create more personalized experiences for customers, and make more strategic business decisions.
Read more on eWeek: 5 Steps to Leveraging Machine Learning: The Shopify Use Case
Types of machine learning
There are many different types of machine learning models. They include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Supervised learning
In supervised learning, algorithms are trained using labeled data points or features. Supervised learning is done by building a model, also known as a classifier, from labeled training data.
These features can be numbers or something else like images or text, called vectors. The goal is for an algorithm to learn a mathematical model that describes how these features correlate with a target label — say, whether that image contains a dog. Examples of machine-learning tasks that involve supervised learning include object classification (identifying whether an image contains a dog) and speech recognition (transcribing spoken words).
Unsupervised learning
Unsupervised learning is an important machine learning area designed to learn from data without supervision. Unlike supervised machine learning, you do not define labels. This means that there is no need for human annotation or structured data.
Unsupervised machine learning techniques attempt to discover underlying structures in unlabeled and unstructured data sources using clustering algorithms, density estimation methods, and matrix factorization techniques, among others.
Semi-supervised learning
Semi-supervised learning is a machine learning task that uses unlabeled data for training. It is part of supervised learning, which includes classification and regression tasks, but unlike supervised learning, not all input data is labeled.
Semi-supervised learning algorithms can be employed when there are limited amounts of labeled data available. It can also be used to improve accuracy by leveraging unlabeled data when it is available.
Reinforcement learning
Reinforcement learning is an area of machine learning that focuses on rewarding appropriate actions and punishing inappropriate ones. Reinforcement-learning algorithms are characterized by their ability to determine how best to achieve a particular goal with limited information about whether the actions they take in any situation will lead to success or failure. That ability forms a core part of their value as human decision-making tools.
Machine learning use cases
Machine learning can be used in various industries, including finance, insurance, health care, education, and others. ML is used in self-driving cars, fraud detection, video gaming AI, and more.
Machine learning is especially useful for businesses that rely heavily on data analysis or use large amounts of data to make decisions. For example, social media platforms use machine learning algorithms to determine which posts are most relevant for users and which ads will most likely lead to sales conversions.
Machine learning is also used in DNA sequencing, image recognition, and diagnosis in healthcare. Finance is another area where machine learning has been highly successful—think bots that trade on Wall Street. Machine learning is also commonly applied in consumer technology areas, including email spam filtering, document classification, and recommendation engines.
Read more use cases on Datamation: Machine Learning (ML) Business Use Cases
Business benefits of machine learning
Machine learning allows enterprises to automate time-consuming tasks so humans can focus on what really matters. It gives companies a competitive edge by providing an extra layer of insight into their business operations and customers. It also provides better security through real-time alerts that are triggered based on predicted outcomes. These benefits allow companies to save time and money while boosting productivity and efficiency.
Machine learning is not just about artificial intelligence—it’s about leveraging technology to improve every aspect of a business. In particular, machine learning enables companies to get meaningful insights from big data without manual processes or analysis. This means businesses can use machine learning to streamline existing processes, increase profits, reduce costs, and provide more value for their customers.
Read next: Best Machine Learning Companies for 2022
Source: https://www.cioinsight.com/big-data/what-is-machine-learning/
