What are you doing right now?

I am currently a software engineer at Cisco. I have also been admitted to the University of British Columbia, Canada, and will be joining the batch of 2021 for the Master of Data Science in Computational Linguistics.

Area of interest

I am currently working on recommender systems. Eventually, I want to transition towards Natural Language Processing.

Why did you first sign up with Univ.AI?

Ever since I graduated in 2018, I quickly realized the importance of having a form of specialization under my belt and how it would shape my future career path. In the pursuit of finding my niche, I attempted to study and understand the concepts of ML and AI with the help of MOOC courses but lost the track and motivation quickly. It was not long after that a friend of mine told me about an advert she came across for Univ.AI’s Experience AI conference. The event had a list of very notable speakers on the panel and I couldn’t resist the chance to attend it.

What role has Univ.AI played in your learning journey in Data Science and AI?

One of the major attributes that Univ.AI has offered me is the confidence to pursue a career in this field. I believe having to overcome the hurdles of doubt and fear, especially while venturing into new and unknown areas, is a major accomplishment. Univ.AI not only gave me this newfound confidence but also the clarity in terms of knowledge, practice, and networking with like-minded individuals to take this bold step.

What role has Univ.AI played in your learning journey in Data Science and AI? 

I believe I am still quite early in this long journey of learning and having to apply my skills effectively. But I definitely have a lot more enthusiasm, especially while approaching more advanced topics that I must credit Univ.AI for.

What did you like most about the course?  

I liked how throughout the course, at no point I felt tired or saturated. I say this because I often attended these sessions right after a long day at work. But I think this was because of the ingenious way in which each class was planned out with alternating intervals of theory and practice that kept my focus invested throughout.

If you had to share just one thing that you learned in the course, what would it be?

Of the many things I learned, the one that really stood out for me was the integration of weights and biases (W&B) to assess the performance of the model. The day Prof. Rahul Dave taught us to include it within our python notebooks using the inline function was game-changing in understanding the concept of over-fitting. Although theoretically, over-fitting had already made sense, it was when I observed the loss graph live during the training phase that solidified the concept for me.

What surprised you about the course?

Accessibility! Being able to approach such eminent professors during and after class was quite comforting and helped me keep a very cordial relation with them. I also had the fortune of meeting some amazing friends, which only added to the experience.

Have you seen new opportunities as a result of the course? Do you feel better equipped for new opportunities?

One of the greatest opportunities I had was to apply for my postgraduate education abroad. A result of which I was accepted into the University of British Columbia, Canada. I am ecstatic to join them next year and eventually begin my career in Data Science.

What advice would you give to a new Univ.AI student? 

If I could go back and change anything during my time at Univ.AI, I would ask a lot more questions during the class, and I would give the same advice to any new Univ.AI student. Univ.AI provides a very conducive environment to learning and you should make the best possible use of it.

Any final thoughts or recommendations about the course?

I would highly recommend Univ.AI to anyone who is seriously considering a career in the field of ML/AI. I can attest to the quality of the teaching and the dedication of the team working tirelessly behind the scenes to ensure a world-class experience like no other.