Data Science & Analytics

Machine Learning, Deep Learning, and GenAI

Meet Nirmal Parmar, a highly skilled Data Scientist with a PhD in Chemical Engineering and advanced expertise in data analysis. With a wealth of experience as a researcher on numerous international and national research projects, Nirmal has firmly established himself as an expert in the fields of thermal applications and data-driven solutions. His proficiency extends beyond conventional machine learning methods; he also excels in integrating cutting-edge techniques like mathematical gnostics with machine learning, leading to the development of novel and innovative approaches for analyzing complex datasets.

Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks.[1] It is seen as a broad subfield of artificial intelligence (AI).

Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer's part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step {1].

[1]. Ethem Alpaydin (2020). Introduction to Machine Learning(Fourth ed.). MIT. pp. xix, 1–3, 13–18. ISBN 978-0262043793.