Insights: My Remote Machine Learning Internship
In my last post, I shared my experience as a non-CSE/IT girl from IIT BHU diving into the world of ML and Data Science. Everyone in this Data Science world experiences a story that is unique to themselves. Data Science has encompassed every sector of life, while I am still a novice in Machine Learning and Data Science realms, I feel like I should shed some more light on my ML and Data Science journey with everyone looking to enter this world. Though the journey had its twists and turns, it was one of the best learning phases.
The real driving force behind my journey has always been my fascination with technology. It awed me how some code lines could transform the way people live and create a wave of wonders. Facebook auto-tagging everyone in my uploaded posts, Google Assistant answers all my questions (No matter how silly they were), Netflix recommending movies and series that are most compatible with my likes, amazed me about just how big this field was and the astounding things this technology could achieve.
I still vividly remember how it all began. Out of curiosity, I started reading articles on Medium about a career in Data Science and how it holds the potential to revolutionize our lives. I spent a week gazing at my computer screen for hours to find, read, and understand all the material I could get my hands on!
Almost all the articles I came across suggested that the best way to get started with Data Science is by pursuing Andrew Ng’s, Machine Learning course on Coursera. So because it was popular opinion, I decided to hit off with it. Believe me; I didn’t understand a word in the initial weeks. But what my experience says is: learning concepts and technical jargon are not the fundamentals of ML. Implementing models to tackle real-life problems is where the significant portion of learning lies.
Being a sophomore in Civil Engineering, I started searching for ML applications that can be applied to the core field. I undertook an interdisciplinary project encompassing my strong suits: Civil Engineering, ML, and AI. It proved to be a great learning experience as a newbie in the field, combining both worlds’ best. Within no time, I started looking out for opportunities that would allow me to explore this field. What’s a better way to do that other than Internships?
Internships are a great way to get your hands dirty and explore the essence of the field. They help you gain the experience of making your code come alive and change the way people use a platform/website. As an intern, you can learn from professionals, gain practical experience in their field, and build a strong professional network, which comes very handy in the long run.
I came across all kinds of advice when I was looking for a Data Science internship. There’s no shortage of people espousing the value of internships in Data Science. So, don’t worry if you don’t have any previous experience. That’s why you are looking for an internship — to gain experience in a practical setting to apply your skills and create a place for yourself in that field.
Data science internships don’t happen overnight. They need time and effort. You need to work on many things before you can start applying to become a Data Science intern. You need to ensure that your resume is up-to-date. Your cover letter is customized according to every company and job description. It would help if you were aware of appropriate interview etiquette, among other things. Some great platforms can help you prepare for an internship interview; among them are GeeksforGeeks (strongly recommended), Analytics Vidhya, etc.
Here’s a gist of my first dive into the ocean of Machine Learning:
Before getting started, I want to put it out that incredible things usually happen when you are least expecting them to happen, so hang in there and keep learning. Data Science is a game of Perseverance!
So here’s my story.
It was a typical mundane day; I was busy fiddling with my phone playing Sudoku and listening to my favorite playlist, unknown to the events’ future course. I received a call; the person on the other end was an HR representative informing me about a job vacancy in a company that I had applied a few days back.
She informed me that I would have an interview tomorrow. Mixed feelings of shock, happiness, and confusion were bubbling up inside me. This was the first time I was ever going to face a panel of interviewers; all the movie scenes started to run inside my head and not for long before I ended in being rejected and dejected. I was unprepared for it both mentally and physically. I started preparing myself for the interview by watching videos on YouTube, reading articles, and consulting with some of my seniors and asking them how to ace an interview, basically cramming everything that I can and trying to revise all that I can. Us raat apun pura raat nahi soya and roya!
The telephonic interview was a technical one; consisting of many theoretical questions and my solutions to real-life problems concerning ML. It lasted half an hour, and after it ended, I didn’t care about what the result would be. I was relieved that this nightmare of interview for me was over.
I received a call the next day announcing my journey as a Data Science intern in the company. I was ecstatic to the core, as this was the last thing I had expected. Us raat bhi apun bilkul nahi soya kyuki itni khushi bahut samay baad hui thi!!
JVR is a research-based, cutting-edge HR Tech platform that strives to establish a healthy ecosystem that bridges the gap between job seekers and job recruiters. It acts as a direct link between job recruiters and job seekers. Its uniqueness is the filtration of White-Collar and BlueCollar applicants as well as jobs.
Within the first few days, I was introduced to my fantastic bunch of colleagues and presented my projects. One of the things that I liked the most about my internship was the trust and freedom given to me as an intern to choose the project I was interested in and go all-in!
I worked on 4–5 live projects in 2 months, including but not limited to:
● Developing a Resume Parser for extracting personal information like name, phone number, email, education, and skill set.
● Building a web scraper to extract Job Description by using Beautiful Soup for better understanding the pattern of dataset needed to build the Job Recommendation System.
● Developing a customer segmentation model for segregating the blue, grey, and white-collar industry.
● Researching and developing a Company and Profile-Wise salary prediction model using Catboost and Ensemble Models.
● Developing a Gamified Neuroscience Test for blue-collar jobs to ease the recruitment process by developing three games to identify candidates’ competencies and personality traits using basic HTML5, CSS3, and JavaScript.
The Data Science industry is still very young, and its job description could somehow seem vague and ambiguous to job seekers. It’s perfectly normal not to possess all the skills needed as most of the job description is idealistically created to align with their best expectation.
Be patient. The learning journey does take time. Learn from your journey with relish. Because after all, the question that remains after completing the journey is — Did you learn everything you have wanted to?
