What is Machine Learning (ML)?

 Today’s enterprises are inundated with data. To drive better business decisions, they have to make sense of it. But the sheer volume coupled with complexity makes data difficult to analyze using traditional tools. Building, testing, iterating, and deploying analytical models for identifying patterns and insights in data eats up employees’ time in a way that scales poorly. Machine learning can enable an organization to derive insights quickly as data scales.

Accelerating model deployment with MLOps

Machine learning defined

Machine learning is a subset of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning, without being explicitly programmed, by feeding it large amounts of data.

Machine learning allows computer systems to continuously adjust and enhance themselves as they accrue more “experiences.” Thus, the performance of these systems can be improved by providing larger and more varied datasets to be processed.

Scope of use cases

Machine learning is being used in nearly every industry and business activity. Machine learning helps the logistics industry optimize shipping and delivery routes, the retail industry personalize shopping experiences and manage inventory, manufacturers automate factories, and helps secure organizations everywhere. When a person uses their voice to query their smartphone or speaker, machine learning is used to understand the request, and to help find the result. The scope of use cases for machine learning is vast and constantly expanding. 

Importance of machine learning

The rate of data generation is accelerating every day. The world is creating more data every day than it ever has in its history. It would be nearly impossible to analyze and utilize all that data without machine learning. As such, machine learning is opening an entirely new realm of what humans can do with computers and other machines. Machine learning helps businesses with important functions like fraud detection, identifying security threats, personalization and recommendations, automated customer service through chatbots, transcription and translation, data analysis, and more. Machine learning is also driving the exciting innovation of tomorrow, such as autonomous vehicles, drones, and airplanes, augmented and virtual reality, and robotics. 

What is the difference between machine learning, artificial intelligence, and deep learning?

While artificial intelligence (AI) and machine learning (ML) are often used synonymously, they are not interchangeable terms. 

Artificial intelligence is an area of computer science concerned with building computers and machines that can reason, learn, and act in a way resembling human intelligence, or systems that involve data whose scale exceeds what humans can analyze. The field includes many different disciplines including data analytics, statistics, hardware and software engineering, neuroscience, and even philosophy. 

Whereas artificial intelligence is a broad category of computer science, machine learning is an application of AI that involves training machines to execute a task without being specifically programmed for it. Machine learning is more explicitly used as a means to extract knowledge from data through techniques such as neural networks, supervised and unsupervised learning, decision trees, and linear regression.

Just as machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning. Deep learning works by training neural networks on sets of data. A neural network is a model that uses a system of artificial neurons that are computational nodes used to classify and analyze data. Data is fed into the first layer of a neural network, with each node making a decision, and then passing that information onto multiple nodes in the next layer. Training models with more than three layers are referred to as “deep neural networks” or “deep learning.” Some modern neural networks have hundreds or thousands of layers.