Machine Learning and Data Science is one of the hottest topics of all the disciplines these days. It has created a lot of interest among the people. Machine Learning has immense potential and we keep seeing a lot of jaw dropping accomplishments day by day. Here I’ll share my understanding of the field and what would it take to make a career in Machine Learning/Data Science. I am hoping that this can be of little help in taking the important decision about the career.
The hype of the topic to some extent is true. The hype and all the buzz shouldn’t be a trigger to choose Machine Learning as a career option. The buzz and the glamour that pulls one into the field soon fades away as soon as he/she gets the feet wet in Machine Learning unless there is a strong objective/goal that Machine Learning will help achieve. Choosing/making a career in Machine Learning shouldn’t be the objective in itself.
I first heard about Machine Learning 3 years ago and I didn’t see any compelling reason at that point of time to seriously consider the field. In early 2016, I came to know about something which forever changed what Machine Learning means to me. I read about diagnosing diabetic retinopathy from retina images using Machine Learning. That was a wow moment for me. I saw immense potential of doing great work, positively impacting people’s lives which is visible first hand, so direct and close from the work I can do. The mere thought that Machine Learning cuts across multiple fields, disciplines and different walks of life is fascinating to me. That’s when I became serious about Machine Learning.
Once you have a solid reason and a goal, below is a short one liner depiction of the path to Machine Learning mastery. Though shortcuts may be taken from time to time, every shortcut will have it’s own cost which will need to be paid by taking a break and coming back to learning. Honestly, I’m yet to complete my first iteration of the loop even after almost 18 months.
Linear Algebra -> Calculus -> Probability -> Statistics -> Statistical Learning Theory -> Optimization Techniques -> Machine Learning/Deep Learning -> Programming Language -> Data Preprocessing, Analysis and Exploratory Data Analysis -> Mastering Machine Learning/Deep Learning libraries/frameworks -> relentless practice -> keeping up with the advancements -> loop back to the topic that you realize you need to revisit.
If you are at a juncture where you are thinking of career options, here’s my advice. Assess yourself, you are the best person to evaluate for yourself what you can do. You don’t have to go by the hype and ride the same wave everyone is. You can achieve mastery, do great work, make a lasting impact on the world, be proud of yourself and make your loved ones proud of you in any field. It need not be Machine Learning (or any hottest happening). Some of the fields that I think are going to be big…