A Comprehensive Survey on Machine Learning
Keywords:Machine Learning, Regression, Classification, Clustering
The objective of this briefing is to present an overview of the topic, machine learning techniques currently in use or in consideration at statistical agencies worldwide. It is important to know the main reason why real-world scenarios should start exploring the use of machine learning techniques, terminology, approach and about few popular libraries in python, what regression is, by completely throwing light on simple as well as multiple linear and non-linear regression models and their applications, classification techniques, various clustering techniques. The material presented in this paper is the result of a study based on different models and the study of various datasets (analysis and choice of the correct model are important). While Machine Learning involves concepts of automation, it requires human guidance. Machine Learning involves a high level of generalization to get a system that performs well on yet-unseen data instances. Topics like regression, classification, and clustering, the report covers the insight of various techniques and their applications.
S. A. Macskassy and F. Provost, “Classification in networked data: A toolkit and a univariate case study,” The Journal of Machine Learn-ing Research, vol. 8, pp. 935–983, 2007.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,” Commun. ACM, vol. 64, no. 3, pp. 107–115, 2021.
S. Subudhi, R. N. Patro, and P. K. Biswal, “Superpixel clustering based segmentation algorithm for hyperspectral image classification,” in 2019 International Conference on Information Technology (ICIT), 2019.
R. Gandhi, “Introduction to machine learning algorithms: Linear regression,” Towards Data Science, 27-May-2018. [Online]. Available: https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a. [Accessed:11-Jan-2021].
M. Summerfield, Programming in python 3: A complete introduction to the python language, 2nd ed. Boston, MA: Addison-Wesley Educational, 2010.
W. McKinney, Python for Data Analysis, 2e. Sebastopol, CA: O’Reilly Media, 2017.
S. Nabwire, H.-K. Suh, M. S. Kim, I. Baek, and B.-K. Cho, “Review: Application of artificial intelligence in phenomics,” Sensors (Basel), vol. 21, no. 13, 2021.
A. Ganguly, IBM Watson solutions for machine learning: Achieving successful results across computer vision, natural language processing and AI projects using Watson cognitive tools. New Delhi, India: BPB Publications, 2021.
T. Joachims. Transductive inference for text classiﬁcation using support vector machines. In Proceedings of the International Conference on Machine Learning (ICML’99), pp. 200–209, 1999.
N. Zulkarnain and M. Anshari, “Big data: Concept, applications, & challenges,” in 2016 International Conference on Information Management and Technology (ICIMTech), 2016.
I. Arel, D. C. Rose and T. P. Karnowski, "Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]," in IEEE Computational Intelligence Magazine, vol. 5, no. 4, pp. 13-18, Nov. 2010, doi: 10.1109/MCI.2010.938364.
Y. Low, J. E. Gonzalez, A. Kyrola, D. Bickson, C. E. Guestrin, and J. Hellerstein, “GraphLab: A new framework for parallel machine learning,” arXiv [cs.LG], 2014.
B. Singh, P. Sihag, S. M. Pandhiani, S. Debnath, and S. Gautam, “Estimation of permeability of soil using easy measured soil parameters: assessing the artificial intelligence-based models,” ISH j. hydraul. eng., pp. 1–11, 2019.
A. Sharma, P. Agrawal, V. Madaan, and S. Goyal, “Prediction on diabetes patient’s hospital readmission rates,” in Proceedings of the Third International Conference on Advanced Informatics for Computing Research - ICAICR ’19, 2019.
How to Cite
This work is licensed under a Creative Commons Attribution 4.0 International License.