Main image of article Will Machine Learning Jobs Eventually Go Mainstream?

Machine learning (ML), a subdiscipline of artificial intelligence (A.I.), is a complicated skill to learn. However, experts predict that the need for machine-learning skills will only increase over the next decade. Is that true? And if so, how can you begin to learn enough about ML to potentially land a job?

According to Burning Glass, which collects and analyzes millions of job postings from across the country, jobs that heavily involve machine learning are predicted to grow 76.3 percent over the next 10 years. More than 220,000 job postings over the past 12 months mentioned “machine learning” in a meaningful way—quite a large number for a “niche” technology. 

Burning Glass also estimates that ML jobs earn a median salary of $107,000. Some 76.5 percent of those who work with machine learning have a bachelor’s degree, which is good news—you don’t necessarily need a highly advanced degree to find a role in this discipline. If you’re intent on pursuing a machine-learning education, it’s also helpful to study Python, the principles of data science and A.I., SQL, Java, and C++, as all of those skills and languages pop up frequently in job postings related to machine learning.

Those new to ML and A.I. might want to stop off by Hacker Noon, which has a useful breakdown of A.I. from a programmer’s perspective. Once you’ve locked down some basic terminology, consider swinging by Microsoft’s AI School, which offers lessons in everything from text analytics and object recognition to custom neural-network models. 

OpenAI, a sorta-non-profit (it’s complicated) dedicated to responsible A.I. and ML, offers models and tools for training A.I. and machine learning if you’re a hands-on learner. The OpenAI website also has a very extensive tutorial in deep reinforcement learning.

Feeling more advanced? Check out Bloomberg’s Foundations of Machine Learning, a free online course. You’ll need to be very familiar with data structures and algorithms to follow along. Once you’ve mastered the skills presented there, you’ll likely have a solid foundation for applying ML to your workflows.