With a Bioinformatics background and a doctoral degree in Biotechnology, I am currently working as a Research Associate at the University of Hyderabad, where I deal with computational analysis of large-scale genome sequence data. With the large-scale data, we have started to deal with in our research lab, it has become imperative for us to adapt to more advanced computational concepts rather than relying on simple scripts and plots to draw inferences. This motivated me to learn machine learning methods. And with a lot of searching, I have found Great learning’s PGP AIML to be a great platform to upskill myself with these concepts.
I was overwhelmed with the analysis of large-scale high-dimensional biological data that we had to analyze at work. Though I knew applying machine learning techniques could help me in the analysis, I was not sure about how and where to start upskilling myself. That is when I chose to join this program. I have previously followed videos for Dr. Abhinanda Sarkar and absolutely loved his way of teaching, which has motivated me to check about Great Learning. I did not have any apprehensions about learning online. Rather, it was comfortable for me to learn at my own pace.
In addition to the recorded content, weekly mentored learning sessions were of great help in further understanding the concepts as well as clearing our doubts. Our mentor, Thanish, is absolutely amazing and approachable. He helped me with queries regarding the application of ML in my work as well, which was of great help to me. The quality of the mentored learning sessions was absolutely amazing. Thanish made sure we understood the concepts well and was very patient with us in answering all our queries. He also helped us further by sharing extra content and blogs. He played a huge role in simplifying the concepts and explaining them with real-time examples. He was always available for any ML-related queries. I would really like to thank him for his immense support throughout my GL journey.
I can now apply these newly learned skills in my research work, and this has helped me immensely in understanding the underlying patterns in the data and gaining appreciation from my peer group.
Machine learning/Deep Learning requires a parallel focus on understanding the theoretical concepts as well as application of the same practically. Understanding concepts from multi-disciplinary areas such as statistics, programming, etc., could be overwhelming at the beginning, but I suggest the beginners hang on and push themselves. Analytics can be fun and interesting and is for someone who is always inquisitive.
