CHALLENGES AND APPROACHES IN MACHINE LEARNING WITH BIG DATA

Miss.Manisha Ghode; Mr. Lowlesh Yadav; Mr. Neehal Jiwane

doi:10.59367/9f44fw60

Authors

Miss.Manisha Ghode Department of Computer Science & Engineering, Shri Sai College of Engineering & Technology, Chandrapur, India.
Mr. Lowlesh Yadav Assistant Professor, Department of Computer Science & Engineering, Shri Sai College of Engineering & Technology, Chandrapur, India.
Mr. Neehal Jiwane Department of Computer Science and Engineering, Technology, Shri Sai, College Chandrapur, India

DOI:

https://doi.org/10.59367/9f44fw60

Keywords:

Machine Learning, Big Data, Literacy, Challenges Approaches

Abstract

The Big Data revolution promises to transfigure how we live, work, and suppose by enabling process optimization, empowering sapience discovery and perfecting decision timber. The consummation of this grand eventuality relies on the capability to prize value from similar massive data through data analytics; machine literacy is at its core because of its capability to learn from data and give data driven perceptivity, opinions, and prognostications. still, traditional machine learning approaches were developed in a different period, and therefore are grounded upon multiple hypotheticals, similar as the data set befitting entirely into memory, what unfortunately no longer holds true in this new environment. These broken hypotheticals, together with the Big Data characteristics, are creating obstacles for the traditional ways. Accordingly, this paper compiles, summarizes, and organizes machine literacy challenges with Big Data. In discrepancy to other exploration that discusses challenges, this work highlights the cause – effect relationship by organizing challenges according to Big Data Vs or confines that instigated the issue volume, haste, variety, or veracity. also, arising machine learning approaches and ways are bandied in terms of how they're able of handling the colorful challenges with the ultimate ideal of helping interpreters elect applicable results for their use cases. Eventually, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the sphere, identifies exploration gaps and openings, and provides a strong foundation and stimulant for farther exploration in the field of machine literacy with Big Data.

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