What is Machine Learning?

Machine learning (ML) is the subset of artificial intelligence (AI) that focuses on building systems that learn—or improve performance—based on the data they consume. Artificial intelligence is a broad term that refers to systems or machines that mimic human intelligence. Machine learning and AI are often discussed together, and the terms are sometimes used interchangeably, but they don’t mean the same thing. An important distinction is that although all machine learnings are AI, not all AI’s are machine learning.

 

Today, it is at work all around us. When we interact with banks, shop online, or use social media, machine learning’s algorithms come into play to make our experience efficient, smooth, and secure. The technology around it are developing rapidly, and we’re just beginning to scratch the surface of its capabilities.

Types of Machine learning

Algorithms are the engines that power machine learnings. In general, two major types of machine learning’s algorithms are used today: supervised learning and unsupervised learning. The difference between them is defined by how each learns about data to make predictions.

Also read: what is Data Science

Supervised Learning: These algorithms are the commonly used. With this model, a data scientist acts as a guide and teaches the algorithm what conclusions it should make. Just as a child learns to identify fruits by memorizing them in a picture book, in supervised learning, the algorithm is trained by a dataset that is already labeled and has a predefined output.

Unsupervised Learning : These uses a more independent approach, in which a computer learns to identify complex processes and patterns without a human providing close, constant guidance. Unsupervised learning involves training based on data that does not have labels or a specific, defined out.

Also Read: how to start a career in data science

Machine learning’s essentials and developers

When getting started with machine learning, developers will rely on their knowledge of statistics, probability, and calculus to most successfully create models that learn over time. With sharp skills in these areas, developers should have no problem learning the tools many other developers use to train modern ML algorithms. Developers also can make decisions about whether their algorithms will be supervised or unsupervised. It’s possible for a developer to make decisions and set up a model early on in a project, then allow the model to learn without much further developer involvement.

There is often a blurry line between developer and data scientist. Sometimes developers will synthesize data from a  model, while data scientists will contribute to developing solutions for the end user. Collaboration between these two disciplines can make ML projects more valuable and useful.

Machine learning use cases

This field powers a variety of key businesses use case. But how does it deliver competitive advantage? Among machine learning’s most compelling qualities is its ability to automate and speed time to decision and accelerate time to value. That starts with gaining better business visibility and enhancing collaboration.

“By embedding machine learning, finance can work faster and smarter, and pick up where the machine left off,” Clayton says.

Machine learning potential

This field offers tremendous potential to help organizations derive business value from the wealth of data available today. However, inefficient workflows can hold companies back from realizing machine learning’s maximum potential.

 

To succeed at an enterprise level, machine learning needs to be part of a comprehensive platform that helps organizations simplify operations and deploy models at scale. The right solution will enable organizations to centralize all data science work in a collaborative platform and accelerate the use and management of open source tools, frameworks, and infrastructure.

 

 

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