Generative AI and ChatGPT - All you wanted to know....

Harit Naik Director - Global Innovation & Knowledge Management

Initially, I wasn’t sure what to expect from this course. Coming from a production engineering background, AI always felt a bit abstract, but the way it was tied to real industrial problems made it click. Topics like predictive maintenance using machine learning and computer vision for defect detection were especially relevant, since similar issues show up on our shop floor. The section on data preprocessing and feature selection was something I didn’t realize I was missing, and it filled a clear knowledge gap from my earlier, more theory-heavy exposure to AI. One challenge was wrapping my head around model selection trade-offs, especially when comparing neural networks versus simpler models for limited datasets. The course didn’t hide those limitations, which I appreciated. A practical takeaway was learning how to structure an end-to-end AI workflow, from collecting sensor data to validating model outputs before deployment. That directly helped on a small pilot we’re running for anomaly detection on rotating equipment. The content felt grounded in real constraints like data quality and compute limits, not ideal scenarios. It definitely strengthened my technical clarity.

Prerana Renavikar

Coming into this course, I had some prior exposure to the subject, mostly from dabbling with Python scripts and a few proof‑of‑concept models at work. What helped here was the way core topics like supervised learning (especially regression and classification) were tied directly to engineering use cases. The sections on time‑series forecasting for predictive maintenance and basic computer vision for inspection systems were particularly relevant to a manufacturing project I’m on. One challenge was getting through the model validation and hyperparameter tuning parts. Concepts like cross‑validation and overfitting weren’t new, but applying them correctly with noisy, real sensor data took a few attempts and some backtracking. That struggle actually mirrored what happens on the job, which made it useful rather than frustrating. A practical takeaway was a clear workflow for taking raw operational data, doing feature engineering, and deciding whether a simple model or a neural network is justified. That filled a knowledge gap between theory and what’s realistic under time and compute constraints. Parts of the course were uneven in difficulty, but the examples felt honest. Overall, it felt grounded in real engineering practice.

Ikram Ul Haq

Coming into this course, I had some prior exposure to the subject, mainly around basic machine learning concepts, but not much on applying them in an engineering setting. What stood out was how the course connected supervised learning and neural networks to real industrial problems like predictive maintenance and process optimization. The sections on feature engineering for sensor data and model validation in noisy environments were especially relevant to work I’m doing on equipment health monitoring. One challenge was keeping up with the math behind model tuning while also understanding the practical trade‑offs. The jump from theory to implementation, particularly when covering computer vision for defect detection, took some effort and a bit of extra practice outside the lectures. A practical takeaway was learning how to frame an engineering problem as an AI problem, including when not to use deep learning and stick with simpler models. That alone helped fill a knowledge gap around model selection and deployment constraints. Difficulty felt moderate but fair, especially for someone working full time. The content felt aligned with practical engineering demands.

SHARFUDDIN KHAN

Coming into this course, I had some prior exposure to the subject, mostly from dabbling with basic machine learning models on the job. What this course did well was connect AI concepts directly to engineering use cases instead of staying theoretical. The modules on predictive maintenance using time-series data and computer vision for defect detection were especially relevant to a manufacturing project I’m currently involved in. Seeing how feature engineering impacts model performance in real sensor data helped fill a gap I had around why some of our earlier models failed in production. One challenge was keeping up with the pace when the course moved from model training to deployment topics like model validation and monitoring. That transition exposed how messy real industrial data pipelines can be compared to clean examples. Still, working through those limitations made it more realistic. A practical takeaway was learning how to frame AI problems properly—deciding when anomaly detection makes more sense than supervised classification saved us time on a pilot line. The content translated quickly into my day-to-day work, especially during discussions with data and controls teams. It definitely strengthened my technical clarity.