Machine learning
DEFINITION
What is machine learning and how does it work? In-depth guide
Machine learning is widely applicable across many industries. recommendation engine for example, are used by e-commerce, social media and news organizations to suggest content based on a customer's past behaviour. Machine learning algorithms and machine vision are a critical component of self-driving cars, helping them navigate the roads safely. In healthcare, machine learning is used to diagnose and suggest treatment plans. Other common include fraud detection, spam filtering, malware threat detection, predictive maintenance and business process automation.
While machine learning is a powerful tool for solving problems, improving business operations and automating tasks, it's also a complex and challenging technology, requiring deep expertise and significant resources. Choosing the right algorithm for a task calls for a strong grasp of mathematics and statistics. Training machine learning Algorithm often involves large amounts of good quality data to produce accurate results. The results themselves can be difficult to understand -- particularly the outcomes produced by complex algorithms, such as the deep learning patterned after the human brain. And can be costly to run and tune.
Still, most organizations either directly or indirectly through ML-infused products are embracing machine learning. According to the "2023 AI and Machine Learning Research Report" from Rack space Technology, 72% of companies surveyed said that AI and machine learning are part of their IT and business strategies, and 69% described AI/ML as the most important technology. Companies that have adopted it reported using it to improve existing processes (67%), predict business performance and industry trends (60%) and reduce risk (53%).
TechTarget's guide to machine learning is a primer on this important field of computer science, further explaining what machine learning is, how to do it and how it is applied in business. You'll find information on the various types of machine learning algorithms, the challenges and best practices associated with developing and and what the future holds for machine learning. Throughout the guide, there are hyperlinks to related articles that cover the topics in greater depth.
Why is machine learning important?
Machine learning has played a progressively central role in human society since its beginning in the mid 20th century when AI pioneers like Walter Pitts, Warren McCulloch, Alan Turing and John von Neumann laid the groundwork for computation. The training of machines to learn from data and improve over time has enabled organizations to automate routine tasks that were previously done by humans -- in principle, freeing us up for more creative and strategic work.
Machine learning also performs manual tasks that are beyond our ability to execute at scale -- for example, processing the huge quantities of data generated today by digital devices. Machine learning's ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields ranging from finance and retail to healthcare and scientific discovery. Many of today's leading companies, including Facebook, Google and Uber, make machine learning a central part of their operations.
As the volume of data generated by modern societies continues to proliferate, machine learning will likely become even more vital to humans and essential to machine intelligence itself. The technology not only helps us make sense of the data we create, but synergistically the abundance of data we create further strengthens ML's data-driven learning capabilities.
What will come of this continuous learning loop? Machine learning is a pathway to Artificial Intelligence which in turn fuels advancements in ML that likewise improve AI and progressively blur the boundaries between machine intelligence and human intellect.
What are the different types of machine learning?
Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are ---------------------: supervised learning, unsupervised learning, semi supervised learning and reinforcement learning.
The type of algorithm data scientists choose depends on the nature of the data. Many of the algorithms and techniques aren't limited to just one of the primary ML types listed here. They're often adapted to multiple types, depending on the problem to be solved and the data set. For instance, deep learning algorithms such as conversational neural networks and recurrent neural networks are used in supervised, unsupervised and reinforcement learning tasks, based on the specific problem and availability of data.