A Review of Machine Learning Techniques: Basic Algorithms and Use Cases

  • Unique Paper ID: 169912
  • PageNo: 2730-2732
  • Abstract:
  • Machine learning (ML) has become a cornerstone of modern data-driven technologies, providing systems the ability to automatically learn and improve from experience without being explicitly programmed. This paper reviews the basic machine learning techniques and algorithms, including supervised, unsupervised, and reinforcement learning. Key algorithms such as linear regression, decision trees, support vector machines (SVM), k-means clustering, and neural networks are discussed. The paper also explores various real-world use cases of machine learning across industries such as healthcare, finance, marketing, and transportation, providing a comprehensive overview of its impact and potential challenges.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{169912,
        author = {Prerna Sharma and Anant Garg and Abrar and Dr.Anamika Ahirwar},
        title = {A Review of Machine Learning Techniques: Basic Algorithms and Use Cases},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2730-2732},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169912},
        abstract = {Machine learning (ML) has become a cornerstone of modern data-driven technologies, providing systems the ability to automatically learn and improve from experience without being explicitly programmed. This paper reviews the basic machine learning techniques and algorithms, including supervised, unsupervised, and reinforcement learning. Key algorithms such as linear regression, decision trees, support vector machines (SVM), k-means clustering, and neural networks are discussed. The paper also explores various real-world use cases of machine learning across industries such as healthcare, finance, marketing, and transportation, providing a comprehensive overview of its impact and potential challenges.},
        keywords = {Machine learning (ML), Support Vector Machines (SVM), Linear Regression.},
        month = {November},
        }

Cite This Article

Sharma, P., & Garg, A., & Abrar, , & Ahirwar, D. (2024). A Review of Machine Learning Techniques: Basic Algorithms and Use Cases. International Journal of Innovative Research in Technology (IJIRT), 11(6), 2730–2732.

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