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ICDSMLA 2021 pp 73–81Cite as

Supervised and Unsupervised Machine Learning Approaches—A Survey

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 947)

Abstract

Machine learning task is broadly divided into supervised and unsupervised approaches. In supervised learning, output is already known and we have to train the model by giving lot of data called labeled dataset to train our model. The main goal is to predict the outcome. It includes regression and classification problem. In unsupervised learning, no output mapping with input as well as it is independent in nature. The dataset used in unsupervised machine learning is unlabeled. The main focus of this paper is to give detailed understanding of supervised and unsupervised machine learning algorithm with pseudocodes.

Keywords

  • Supervised
  • Unsupervised
  • Decision tree
  • K-means
  • PCA

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Author information

Authors and Affiliations

  1. GCET, Hyderabad, India

    C. Esther Varma & Puja S. Prasad

Authors
  1. C. Esther Varma
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  2. Puja S. Prasad
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Corresponding author

Correspondence to C. Esther Varma .

Editor information

Editors and Affiliations

  1. BioAxis DNA Research Centre Private Ltd., Hyderabad, Telangana, India

    Amit Kumar

  2. Department of Computer Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Salerno, Italy

    Sabrina Senatore

  3. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India

    Vinit Kumar Gunjan

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Esther Varma, C., Prasad, P.S. (2023). Supervised and Unsupervised Machine Learning Approaches—A Survey. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2021. Lecture Notes in Electrical Engineering, vol 947. Springer, Singapore. https://doi.org/10.1007/978-981-19-5936-3_7

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  • DOI: https://doi.org/10.1007/978-981-19-5936-3_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5935-6

  • Online ISBN: 978-981-19-5936-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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