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State-of-the-art Machine Learning Techniques: 360 Degree Overview

EasyChair Preprint no. 3931

19 pagesDate: July 23, 2020

Abstract

Machine learning is the investigation of getting PCs to act without being unequivocally changed. In the previous decade, machine learning has given us self-driving vehicles, pragmatic discourse acknowledgment, viable web search, and an unfathomably improved comprehension of the human genome. Machine learning is so unavoidable today that you most likely use it many times each day without knowing it. Numerous specialists likewise think it is the most ideal approach to machine learning ground towards human-level machine learning. Along with numerous different controls, machine learning strategies have been broadly utilized in bioinformatics. The challenges and cost of organic investigations have prompted the improvement of refined machine learning approaches for this application zone. This paper will state the different machine learning applications expected to run the machine learning ventures. The fundamental limitation is the primary methodologies and contextual investigations of utilizing machine learning for determining in various zones. In this paper, we try to provide an overview of ML techniques from all perspectives.

Keyphrases: Artificial Neural Network, machine learning, Machine Learning Algorithms, supervised learning, Support Vector Machine, unsupervised learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:3931,
  author = {Supriya Anand and Shilpa Gite},
  title = {State-of-the-art Machine Learning Techniques: 360 Degree Overview},
  howpublished = {EasyChair Preprint no. 3931},

  year = {EasyChair, 2020}}
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