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Publication: IIP Proceedings Year: 2022, Month: November Page No: 244-250, ISSN/ISBN: 978-81-959356-3-5, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023D9AF9B329B084DB.pdf
MACHINE LEARNING ALGORITHMS
Area/Stream: Computing Technologies and Data Sciences, Authors: Rajani Rajalingam ,Dr. Madhusudhana Reddy Barusu,G. Prathibha Priyadarshini,Pulagouni Priyanka Keywords: Artificial Intelligence, Machine learning, Regression, Classification, Support Vector Machine. Book Name /series: Futuristic Trends in Computing Technologies and Data Sciences,Volume 2, Book 18, Part 4, Chapter 4Publication: IIP Proceedings Year: 2022, Month: November Page No: 244-250, ISSN/ISBN: 978-81-959356-3-5, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023D9AF9B329B084DB.pdf
Abstract:
Now-a-days, everyone is familiar with the term “data” and it is everywhere. But, this is huge in size and may be generated by people or devices. The problem with data is that, it could be in different forms like text, audio, video, and image etc., Due to this the data can be categorized as structured or unstructured. Analyzing and producing results out of this unstructured data is a time-consuming process. However, it would be easy to derive output from unbalanced data if it could be converted into balanced data. Here comes the role of Machine Learning, which is a subset of Artificial Intelligence (AI) that enables machines or other systems to learn on their own without any kind of explicit programming. These systems are designed in such a way that, they use knowledge to extract information from the unbalanced data. To deal with these data problems, various techniques have been supported by machine learning. For instance, to develop decision–making insights, many data-intensive problems require implementation of regression or classification techniques. This falls within the area of machine learning. Machine learning algorithms can be categorized as supervised, unsupervised and reinforcement learning strategies based on the desired outcome of the algorithm. Examples of various Machine learning algorithms include Linear Regression, Logistic regression, k-nearest neighbors, k-means, Naïve Bayes, Support Vector Machine (SVM), Random forest, Decision tree, Dimensionality reduction, Gradient boosting and Ada Boosting algorithm etc., could be applied on data for future predictions.
Cite this: Rajani Rajalingam ,Dr. Madhusudhana Reddy Barusu,G. Prathibha Priyadarshini,Pulagouni Priyanka ,"MACHINE LEARNING ALGORITHMS", Futuristic Trends in Computing Technologies and Data Sciences,Volume 2, Book 18, Part 4, Chapter 4, November, 2022, 244-250, 978-81-959356-3-5, https://rsquarel.org/assets/docupload/rsl2023D9AF9B329B084DB.pdf
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