The number of jobs in Artificial Intelligence and Machine Learning is constantly growing. Pursuing a course while working at your full-time job might be challenging, but it isn’t impossible. Read further to learn about Sunaal Dua’s journey with Great Learning’s PGP Artificial Intelligence and Machine Learning Course in his own words.
I have completed three years in IT and have recently transitioned into analytics. I am currently working as a Data Engineer and Analyst with a progressive organization shouldering Data Transformation and Automation pipelines. Before PGP-AIML, I was working as a Backend Web Engineer for approx. 2.5 years. My Technology Stack before pursuing the course was: PHP-Laravel, Python-Django, MySQL, JavaScript, and Ajax.
I knew for a fact that prior experience in data modeling and management is a must in the Machine Learning or Data Science space, which I was missing at the time. I never got the chance to deal with Big Data as a backend web engineer. So learning SQL and managing big data was mandatory before/during the course. At the same time, concepts of statistics and probability were important. These are some concepts that we learn in school, so suffice to say that I was out of touch.
Also, pursuing an intensive course like this with a full-time job was a BIG challenge, but with the help of my program manager, peers, and mentor, it all worked out just fine.
Like I said, the transition journey was a bumpy ride. I had previously tried my hands at AIML from online resources, but all in vain. Learning was not structured, concepts were taught intuitively, and I found myself mugging up things. I was looking for more, something mathematically intensive, at a more granular level. Because of all this I knew, I’d be facing more challenges in my transition and would have to spend a lot of extra hours.
ML and data science space is one of the hottest topics in the world right now, hence better opportunities. Also, I grew old from my previous profile, felt stagnant, and after a point was doing it just because it paid the bills, not because I liked it. I knew I had to keep with the pace of the world. That’s why I was desperately looking for a transition into this field.
The transition process was intense. I had to do a lot of things in a lot less time. Regardless, when I was confident of my skills, I started giving interviews, and they had always been nerve-wracking. I applied to hundreds of companies in different profiles like ML, Data Analyst, Data Scientist, Data Engineer, etc. In 3-4 months, I was able to clear three companies. Finally accepted an offer as an Analyst.
Usually, there are 3 to 4 rounds in the interview, starting with the coding round, then face-to-face and technical rounds.
The whole program is so well structured that it automatically works. Also, I never limited myself to theoretical knowledge only. By the second module, I started “Kaggling.” In the whole duration of the course, I managed to do 18 good Kaggle projects, including the projects in the course. Kaggle gives you close to real-time job experience. I think it’s highly recommended. It forces you to learn more and more techniques that are used in real-time. But before that, one needs to have their basics clear. Only then can you learn the advanced stuff. That’s where Great Learning helped me. Packed with great mathematical knowledge of algorithms, I was easily able to grab advanced concepts in ML and Data Science.
My mentor was also amazing. He went out of the way to help me. He solved my queries outside the curriculum also, helped me whenever I got stuck on a Kaggle project. Even after the course, we are still connected, and I still ask him whenever I get stuck somewhere. Great Learning is a great blend of mentorship, theory, and practice. There were times when I required extra studying material. My program manager actively helped in providing the extra resources whenever I wanted one.
The “Excelerate” feature helped me get the job. I got my resume checked and rechecked multiple times, and recently I made use of my 1 out of 2 counseling session. I accepted the offer from the Great Learning job portal only.
It is really exciting. I have completed five months in the new role, and I am thoroughly enjoying it. From here on, I’ll be looking for a more dedicated role in Machine Learning. Experience in analytics goes a long way. That’s what I am gaining right now. The next stop is a full-time Machine Learning role.
For people who are starting with this journey, I would suggest that they learn things with a mathematical perspective, not just intuition. Also, I would highly recommend Kaggle if you have no prior experience in the AI/ML space.
