My Expectations of ITCC508
- What have you initially learned about ML, DL, and NLP?
From what I read, it seems that Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) are tools that have vital to the successful innovation. ML has proven to save money and time, help in smart decision making, enhance customer experience, and improve productivity and growth for businesses[1]. DL has also improved the business processes related to customer service, sales, marketing, risks management, and an organization's daily operations[2]. Meanwhile, NLP is still a rapidly growing technology, though it has since proven to be useful in many fields including quantum mechanics, chemistry, and cybersecurity[3]. A deeper dive into NLP's definition reveals that it isn't just limited to virtual assistants that I initially thought. NLP could also be used in email spam filtering, sentiment analysis, document summarization, and text / speech translation[4].
- What do you expect to further learn about Machine Learning (ML.), Deep Learning (DL.), and NLP on this course?
For this semester, I expect to expand my knowledge of these machine learning algorithms, particularly when integrated with NLP. I'm intrigued to find out on how these seemingly unstructured data can be quantified in such a way that computer algorithms can utilize it to learn and make predictions out of it. Other than that, I expect to learn more about how these algorithms can be fitted to provide insightful predictions on various domains.
- How do you anticipate these topics will relate to your future career as an IT Professional (e.g., in software development, data analysis, system administration)?
I see myself making use of these topics when I enter the industry, especially in this day and age where organizations pour a lot of resources into integrating Artificial Intelligence into their business processes. NLP has proven to be a handy tool in streamlining software development processes, while I can see myself making use of Machine Learning and Deep Learning in systems and networks administration, particularly in fault detection and risk assessment for crucial systems. Meanwhile, continuous improvement in machine learning algorithms has allowed data analysis to be a more robust tool for making data-driven decisions. I can see more organizations develop their customized AI-powered systems to help give them insights on proper decisions that can allow organizations to grow.
References
[1] V. P. Sriram, K. S. Lakshmi, V. Podile, M. Naved, and K. S. Kumar, “Role of Machine Learning and Their Effect on Business Management in the World Today,” Vidyabharati International Interdisciplinary Research Journal, Special Issue of IVCIMS 2021 (International Virtual Conference on Innovation in Multidisciplinary Studies, Aug. 4–5, 2021) pp. 369–374, Sep. 2021.
[2] Z. Zhong, “Deep learning applications in business activities,” American Journal of Management Science and Engineering, vol. 3, no. 5, p. 38, 2018. doi:10.11648/j.ajmse.20180305.11
[3] J. Sawicki, M. Ganzha, and M. Paprzycki, “The state of the art of Natural Language Processing—a systematic automated review of NLP literature using NLP techniques,” Data Intelligence, vol. 5, no. 3, pp. 707–749, 2023. doi:10.1162/dint_a_00213
[4] C. Eppright, “What is Natural Language Processing (NLP)?,” What is Natural Language Processing (NLP)? | Oracle ASEAN, https://www.oracle.com/asean/artificial-intelligence/what-is-natural-language-processing/ (accessed Jul. 29, 2025).