*Intro para about machine learning*
I was still in school when I first experienced the excitement of coding in C++. My strong response to coding was something I sat up and took note of. Over the years with more exposure and understanding of the discipline, I decided to pursue Computer Science Engineering at the undergraduate level. Into the structured curriculum, I realised how vast the landscape truly was, of which, coding was but one aspect. I had underestimated the field of computer science and for a brief period I had to focus on interpreting and understanding larger definitions, broader ideas, and numerous components. It was important to establish distinctions and also connect concepts from disparate subjects.
While enjoying the pace and the rigour of the course in the first two and a half years, I was yet to identify a specific area or subfield that captured my interest totally. I had worked on Operating System, Compilers, Databases, Data Structures and Algorithms, and more, but failed to see how I could build a fulfilling and sustainable career out of any of these.
In the third year of college I took a course in Data Mining, which introduced me to various supervised and unsupervised methods of learning. I was involved in a project that entailed working on a dataset of car emissions, and classification of the data with the help of the KNIME tool. Although no programming was done, this course marked the beginning of my learning for Machine learning. I learnt about