Python's Indispensability in Artificial Intelligence and Machine Learning: A Review

Abraham, H., et al. (2019). Qiskit: An Open-source Framework for Quantum Computing. Qiskit Documentation. Adetiba, E., John T.M., Akinrinmade, A.A., Moninuola, F.S. , Akintade O.O., and Badejo J.A. (2021). Evolution of artificial intelligence languages – a systematic literature review. Journal of Computer Science. 17(11), 1157-1171. http://dx.doi.org/10.3844/jcssp.2021.1157.1171 Alex Ryabtsev. (2024). 8 Reasons Why Python is Good for AI and ML. Retrieved March 7, 2025, from https://djangostars.com/blog/why-python-is-good-for-artificial-intelligence-andmachine-learning/ Ali, M. (2015). Python data types explained: A beginner’s guide. DataCamp. Retrieved March 24, 2025, from https://www.datacamp.com/blog/python-data-types Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., & Zhang, Y. (2019). AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development, 63(4/5),.1-2 https://doi.org/10.1147/jrd.2019.2942287 Bhargavi. (2022, October 25). Artificial intelligence history, stages, types, and domains. Retrieved from https://sailssoftware.com/artificial-intelligence/ Blackwell, A. (2024). Moral Codes: Designing Alternatives to AI. https://doi.org/10.7551/mitpress/14872.003.0012 Blackwell, Alan. (2024). Explanation and transparency: Beyond no-code/low-code. In Moral Codes (pp. 99–116). The MIT Press. Retrieved from http://dx.doi.org/10.7551/mitpress/14872.003.0010 Borretti, F., & Nathan, P. (2024). Using Emacs as an IDE. Retrieved from https://github.com/LispCookbook/cl-cookbook/blob/master/emacs-ide.md website: https://lispcookbook.github.io/cl-cookbook/emacs-ide.html Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., … Anderljung, M. (2020, April 15). Toward trustworthy AI development: Mechanisms for supporting verifiable claims. Retrieved April 23, 2025, from arXiv.org website: https://arxiv.org/abs/2004.07213 Cao, S., Zeng, Y., Yang, S., & Cao, S. (2021). Research on Python data visualization technology. Journal of Physics: Conference Series, 1757(1), 012122. https://doi.org/10.1088/1742- 6596/1757/1/012122 Chatterjee, S. (2024, February 28). Master float in Python: The ultimate guidebook to precision. Emeritus. Retrieved March 24, 2025, from https://emeritus.org/in/learn/float-in-python/ Chen, Y., & Huang, L. (2025). Conditional statements. Retrieved from Springer, pp 133–152 website: https://link.springer.com/chapter/10.1007/978-981-97-8788-3_4 Dalcin, L. D., Paz, R. R., Kler, P. A., & Cosimo, A. (2011). Parallel distributed computing using Python. Advances in Water Resources, 34(9), 1124–1139. http://dx.doi.org/10.1016/j.advwatres.2011.04.013 Dergano F. (2023). Python's Role in Artificial Intelligence and Machine Learning. Retrieved February 27, 2025, from https://raccomandino.medium.com/pythons-role-in-artificialintelligence-and-machine-learning-b6b97843a307 Gaddis, T. (2024). Step-by-step solution. Vaia. Retrieved from https://www.vaia.com/enus/textbooks/computer-science/starting-out-with-c-from-control-structures-through-objects-8-edition/chapter-5/problem-19-the-statement-or-block-that-is-repeated-isknown- (pp. ) Gangopadhyay, S. (2025). The asynchronous IO revolution: How Python is changing the game. GUVI. https://www.guvi.in/blog/the-asynchronous-io-revolution-how-python-ischanging-the-game/ (Retrieved April 12, 2025) GeeksforGeeks. (2024). Why Python is Called Interpreted Language. GeeksforGeeks. https://www.geeksforgeeks.org/why-python-is-called-interpretedlanguage/Geeksforgeeks (2025). Python Operators. Retrieved from March 25, 2025, from https://www.geeksforgeeks.org/python-operators Ghimire, D. (2020). Comparative study on Python web frameworks: Flask and Django. https://www.theseus.fi/bitstream/handle/10024/339796/Ghimire_Devndra.pdf?sequence= 2 Google Quantum AI Team. (2020). Cirq: A Python Framework for Creating, Editing, and Invoking Noisy Intermediate Scale Quantum (NISQ) Circuits. Cirq Documentation. Gupta, A. (2023). Machine Learning with Python; Retrieved from https://medium.com/@aaditgupta06/machine-learning-with-python-aedc5353a72a Gupta, A. (2020, September 30). 20 most popular Python IDEs in 2024: Code like a pro. Simplilearn. Retrieved from https://www.simplilearn.com/tutorials/pythontutorial/python-ide Gupta, N. (2023, March 27). Meta-programming in Python: Unleashing the power of decorators, metaclasses, and introspection. DataScience with Python?—?NishKoder. Retrieved from https://medium.com/nishkoder/meta-programming-in-python-unleashing-the-power-ofdecorators-metaclasses-and-introspection-e1274c753dc1 Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925 Ihaka, R., & Lang, D. T. (2008, January 1). Back to the Future: Lisp as a base for a statistical computing system. (pp. 5-6) https://www.researchgate.net/publication/227019917_Back_to_the_Future_Lisp_as_a_B ase_for_a_Statistical_Computing_System Ingerman, A., & Ostrowski, K. (2019). TensorFlow Federated: Machine Learning on Decentralized Data. TensorFlow Blog. Jain, A. (2024). Embracing abstraction: A dive into abstract classes in Python. Medium. https://medium.com/@abhishekjainindore24/embracing-abstraction-a-dive-into-abstractclasses-in-python-0faf6d83948d (Retrieved April 12, 2025) Jenifar. (2023). Python exec vs eval. Medium. https://medium.com/@brusooo27/python-exec-vseval-ea949931ee8f (Retrieved April 12, 2025) Jin, H., Song, Q., & Hu, X. (2019). Auto-Keras: An efficient neural architecture search system. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, NY, USA: ACM. Retrieved from http://dx.doi.org/10.1145/3292500.3330648 (pp. 1947) Karabulut, N., & Akyüz, Y. (2023). A Comparison of RPA Tool and Python Programming Language for a BOM Digitization Project in Automobile Industry. Yönetim Bili?im Sistemleri Dergisi, 9(2), 15-26. http://dergipark.gov.tr/ybsKhaled, M. (2023, December 7). Python programming language: A comprehensive overview. Retrieved from ProfileTree Web Design and Digital Marketing website: https://profiletree.com/python-programming-language-comprehensive-overview/ Khare, Y. (2024). Use cases of Python context manager. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2024/06/python-context-manager/ (Retrieved April 12, 2025) Kuchling, A. M. & Zadka, M. (October 2000). "What's New in Python 2.0". Python Software Foundation. Retrieved November 20, 2024, from https://docs.python.org/3/whatsnew/2.0.html Kuhlman, D. (September 2011). A Python Book: Beginning Python, Advanced Python, and Python Exercises. Section 1.1. Retrieved November 20, 2024, from https://www.academia.edu/resource/work/34001890 Kluyver Thomas, Ragan-Kelley Benjamin, Perez Fernando, Granger Brian, Bussonnier Matthias, Frederic Jonathan, … Jupyter Development Team. (2016). Jupyter Notebooks-; a publishing format for reproducible computational workflows. In Positioning and Power in Academic Publishing: Players, Agents, and Agendas (pp.87-90). IOS Press. https://doi.org/10.3233/978-1-61499-649-1-87 Kumar, R. (2024). Power of LISP for language-oriented programming. Retrieved from https://industrywired.com/power-of-lisp-for-language-oriented-programming/ Kumar, N. (2018). Python modules tutorial: Importing, writing, and using them. DataCamp. Retrieved from https://www.datacamp.com/tutorial/modules-in-python Lily Hulatt & Grabriel Frietas, (2024). Programming Paradigms. Retrived from https://www.studysmarter.co.uk/explanations/computer-science/computerprogramming/programming-paradigms/ Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 18. https://doi.org/