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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM36768
Volume:
8 |
7 |
Jul 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
In today’s digital era, businesses are actively generating an astonishing 2.5 quintillion bytes of data every single day. For those of you wondering how much that is—well, there are 18 zeroes at a quintillion! With people using social media platforms, digital communication channels, and various contactless services, it is no surprise that big data continues to grow at a colossal rate. But how can we harness the potential of all this information in the future? And what’s machine learning have to do with it? First of all we have To better understand the future of machine learning, one must be able to differentiate between these 3 concepts deep learning (DL), artificial intelligence (AI) and machine learning (ML) interchangeably. machine learning (ML) In this paper we discuss about the Machine Learning and its future aspects Machine learning is about creating an algorithm that a computer uses to provide valuable insights, with data being its key component. It is unique in developing algorithms that learn from data to solve problems without programming. Like a human, a model learns through experience and improves its accuracy over time.At its core, machine learning is all about creating and implementing algorithms that facilitate these decisions and predictions. These algorithms are designed to improve their performance over time, becoming more accurate and effective as they process more data. this field encompasses various techniques ,such as supervised learning, unsupervised learning ,and reinforcement learning ,each with distinct applications and methodology . In traditional programming, a computer follows a set of predefined instructions to perform a task. However, in machine learning, the computer is given a set of examples (data) and a task to perform, but it’s up to the computer to figure out how to accomplish the task based on the examples it’s given. For instance, if we want a computer to recognize images of cats, we don’t provide it with specific i
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Publication Date
7/26/2024
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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