The global machine learning market, which was valued at $1.58B in 2017 is projected to grow to $20.83B in 2024. That brings with it rapid growth in the number of machine learning jobs all over the world. Along comes high salaries and attractive perks. But, every time there is a buzz around a new phenomenon, there are also myths around it. 

Top 10 Myths around Machine Learning Jobs

In this blog post, we do some myth-busting. Springboard mentors identify the top machine learning jobs myths. From qualifications to communication skills, we’ll explore major misconceptions and see what reality holds.

Myth #1: Machine learning is only for self-driving cars and robots.

Machine learning tends to conjure images of advanced futuristic technologies like Tesla and Amazon drones. But those get maximum press because they’re big and visible. Today, machine learning is used in various applications. The auto-complete on your mobile phone keyboard, the chatbot on the food delivery app, the smart-speaker you talk to — all use various machine learning technologies in myriad ways. This means that there are machine learning careers opening up across industries, use cases and locations.

Myth #2: Machine learning jobs require PhDs.

While this may have been the case in the beginning, it is no longer true. Take this role: Machine learning engineer at Apple in Bangalore. It doesn’t expect a PhD. It asks for a master’s degree but stresses more on the experience of applying machine learning to solve real business problems. As the field evolves, hands-on experience will come to replace an educational qualification. 

Myth #3: Machine learning is a complex field to understand.

Deep learning. Computer vision. Voice recognition. Cloud computing. Neural networks. When you first encounter these and other such terms, it might appear complex. But professionals tackle these complexities every single day, applying them to real-world problems. With structured learning and regular practice, machine learning can be easily understood, even mastered.

Myth #4: ML is difficult to learn.

Anything new might seem difficult to learn. But it doesn’t have to be. There are plenty of resources online where you can learn from. There are many open source tools available for you to practice a wide range of machine learning skills. The community on Github, Kaggle etc. also frequently solve problems and post their lessons online. If you already have some experience/knowledge of data, programming or other related IT fields, career transition might be easier than you fear.

Myth #5: Large data sets are impossible to get for practice.

From universities to private companies, several organizations publish their data sets for aspiring ML engineers to use. There are also several open datasets online. If you’re looking to practice your machine learning chops, lack of data will not be a challenge. Here is a collection of top machine learning datasets to get you started, as well as some projects for inspiration

Myth #6: Machine learning jobs are only available in tech companies.

Even traditional businesses like banks, food & beverage companies and governments are swiftly adopting machine learning these days. As a result, there are in-house machine learning teams in companies across industries, sizes and locations. Citibank, Zomato, Freshworks, Shell and Novartis are a few of the companies that are hiring for data science and machine learning jobs right now.

Myth #7: Machine learning professionals don’t code.

Of course, they do. Strong programming skills are essential for ML professionals. This ranges from coding in Python, R etc. to using existing libraries like Scikit-learn. 

Myth #8: Machine learning jobs are all about knowing the tools.

Automation is an important part of machine learning. Engineers use various tools, scripts and libraries to perform various tasks. But, tools aren’t everything. Machine learning engineers bring together a wide range of skills across statistics, mathematics, logical reasoning, business and other areas to do their job.

Myth #9: ML engineers don’t need communication skills.

When we imagine technologists and researchers, we often think of geeks who spend their entire day with machines, staring at a monitor, typing code. But to make technology solve real-world problems, machine learning engineers need to interact with various stakeholders — customers, sales people, marketers, finance team etc. This needs an array of soft skills like time management, conflict resolution, team work, leadership qualities and more. 

Myth #10: Machine learning can’t be done as a freelancer.

Companies considering ML adoption need consultants to give them direction. Those who are just beginning to adopt machine learning might not have the resources to hire full-timers. Even those who have a team might need super-specialized skills for one-off projects. Given the evolving nature of the machine learning career, and the growing acceptance for remote workers, the opportunities for working on ML as a freelancer are increasing. See our blog post about how to become a freelance machine learning engineer for more.

As machine learning jobs become commonplace, more and more professionals — both fresh graduates and experienced folks — will have the opportunity to pursue a career in the field. Real success is only possible when you have complete clarity about what you’re getting into. While this blog post gives you a clear picture of myths vs. reality, it is only a start. For a mentor-led, project-driven online program in machine learning, consider Springboard. You also get 1:1 mentoring, career coaching and a job guarantee!