Machine Learning Models and Algorithms: Approaches, Applications, and Best Practices
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Article | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-ai-mch-lr-04
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About the Article
Machine Learning (ML) is a core branch of Artificial Intelligence (AI) that enables systems to learn from data, identify patterns, and make predictions with minimal explicit programming. This article provides a structured examination of supervised, unsupervised, reinforcement, and deep learning models, detailing their theoretical foundations, algorithms, and practical applications. Case studies from healthcare, finance, agriculture, and education illustrate the cross-sectoral potential of ML, while the discussion highlights challenges such as overfitting, computational demands, and algorithmic bias. The article concludes with best practices for implementation—including model evaluation, regularization, and transfer learning—emphasizing strategies for effective, ethical, and sustainable adoption in both advanced and low-resource contexts.
Key Topics
- Supervised learning models and algorithms
- Unsupervised learning and clustering methods
- Reinforcement learning and adaptive systems
- Deep learning architectures and neural networks
- Case studies in healthcare, finance, agriculture, and education
- Best practices for ML implementation and governance
Suggested Citation
Kazanskaia, A. N. (2025). Machine Learning Models and Algorithms: Approaches, Applications, and Best Practices. NEYA Global Journal of Non-Profit Studies. Neya Global Publishing. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-04
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