Machine learning has become mainstream in India in the last 3-4 years. If you have been a technology professional at that time, you may have considered switching to a machine learning career. But that’s not an easy jump to make. What machine learning jobs should I apply for? How do I develop the skills? Whom should I speak to for help? All these are commonly asked questions by aspiring machine learning professionals. Today, we’ll answer all these and more.
Top machine learning career advice
Springboard’s mentors tell us their top do’s and don’ts for everyone walking the machine learning career path. First the do’s.
1. Machine Learning Career: Do your research
ML is a vast field including natural language processing, computer vision, deep learning, neural networks and so on. The job roles are also varied; you could be a machine learning engineer, analyst, architect and so on. The tools and techniques of machine learning are applicable across industries, from manufacturing to healthcare. In the world of machine learning, possibilities are endless.
So, do your research and get an understanding of the arena before you jump in. First, read widely, and then zoom in on the areas that you are interested in. For instance, you might start reading about machine learning use cases in the financial sector. Then, you might find fraud prevention using pattern detection interesting, which might lead you to forensic analysis. Without understanding the larger picture through research, you might not be able to answer the question, “what career is right for me”.
2. Machine Learning Career: Brush up on your skills
While machine learning is a new field, it is not entirely unconnected to existing technology domains. For example, Python or Java developers can transfer all their programming skills into their machine learning career, and learn new skills in analytics and model building. On the other hand, career statisticians can bring those skills to machine learning, and learn only programming. The skills you already have can put you in good stead in your machine learning career path.
3. Machine Learning Career: Do the work
The fundamental theory of machine learning is important. But what’s more important is the practice. Don’t spend too much time reading/watching videos on the internet. Once you have a basic understanding of machine learning concepts, start your own personal projects and work on them. This will give you hands-on experience as well as a portfolio to show.
4. Machine Learning Career: Learn to communicate
This might seem like generic advice for any job. But it is especially applicable to ML. In machine learning jobs, you will be solving real-world problems using data. And the data is messy. Being able to clearly communicate how you handled the data and how you devised the solution is extremely important. While you are working on your personal projects, also work on how you’ll communicate about them. Now, let’s move to the don’ts.
5. Don’t be passive
It is common from professionals aspiring to dream about a machine learning career on the side for years while doing their primary job. They tend to keep passively reading about ML while on the commute, participate in conversations sporadically etc. But they will not do anything actively to move the needle forward. If you’re in this category, shake it up.
Take active steps towards building your machine learning career. These need not be big bold moves, small ones will do. For instance, setting aside one hour each day to work on a machine learning project will have a great cumulative impact in six months.
6. Don’t begin learning without a rough structure in mind
Simple Googling “Machine learning online programs” will produce millions of results, with many legitimate options. More often than not, you’ll end up being confused about which one to choose. After reading a handful of articles, you might get overwhelmed and step away. This doesn’t have to be.
Before looking for online courses, make a list of what you want from it. This could include practical learning, video lessons, real-world assignment, capstone project, personal coaching/mentorship etc. Once you’ve identified what you want, it will be easier to shortlist courses.
7. Don’t give up too soon
Nobody said career transitions are easy. This is especially true in new and emerging technologies like machine learning. You might spend 3-4 months struggling with some project and then give up because it is too hard. Don’t do that. A career in machine learning is not a one-off trend. It’s a long-term investment.
Take small steps and keep at it. Have a long-term vision for what you want to achieve, break it into smaller milestones and work towards them. Take it one step at a time.
8. Don’t think you have to do it alone
When you’re going through the internet trying to learn everything there is to learn about machine learning, it can feel lonely. Often, we see students discouraged when this happens. Don’t. Join online machine learning communities — on Twitter, LinkedIn, Reddit, Kaggle etc. — and participate proactively. Seniors are often willing to answer questions, offer career advice and help young professionals.
In fact, our board of machine learning mentors from the world’s pre-eminent organizations like Mahindra, PhonePe, InMobi, RedHat etc. are available as part of the Springboard online learning program to offer career advice through your machine learning journey. In addition to in-depth training in machine learning concepts, real-world case studies and an industry-standard capstone project, you also get 1:1 mentoring, coaching and career counselling as part of the program. Check it out now!