When you make the decision to learn machine learning, one of the first challenges is figuring out where to start. How do you decide which skills to learn? Where to learn them? How to put your skills into practice? And most importantly, how do you know if your background is a good fit? Fortunately, at Springboard, we did the research about this so you don’t have to.
Before jumping in to the “how”, let’s take a look at the “why” behind a career in machine learning.
AI is not just a passing fad—this is the future.
According to Forbes, by 2022 there will be 58 million new jobs in AI and machine learning. So, if job security is important to you, there’s no safer bet.
Data has the power to transform a business, letting you learn more about your market, your competitors, and your customers. However, that’s only if you have the right person for the job. The increasing value of data scientists and machine learning engineers is undeniable right now.
Data from Indeed on average salaries found that machine learning engineers typically reel in around $145,000 each year. That’s among the highest salaries in tech, and those numbers increase with experience.
While there is much more to machine learning jobs than building futuristic robots, there’s no doubt that exploring the most revolutionary technology on our planet is a pretty cool way to make a living. It’s full of possibilities, so you’re sure to have fun.
Now that we’ve covered the “why” behind getting started in a career in machine learning, let’s talk a little bit about the “how”.
Before getting started learning, you need to know what you need to learn. To start, strong technical skills are required for anyone pursuing a career as a machine learning engineer. A software engineering background is a great place to start, especially experience with large codebases that achieved scale in the real world. Some of the most sought-after skills in machine learning are expertise in Python, C, C++ development, excellent algorithm and data structure skills, and demonstrable ability to quickly learn and modify large codebases.
From there, a solid understanding of statistics, statistical models, and machine learning models is important.
Ready to learn the next steps to take to build a career in machine learning? Check out Springboard’s comprehensive guide, How to Build a Career in AI and Machine Learning. In this free guide, you’ll discover the tools you need to master, the different roles available in the industry, and advice from experts already in the field. You’ll also get to work through example interview questions.
Or, if you’re ready to transition to a role in this cutting-edge field but want more guidance, check out Springboard’s Machine Learning Bootcamp. It’s an intensive, mentor-led bootcamp with a job guarantee!
|Locations:||Online, San Francisco|
|Courses:||Data Analytics Career Track, UX Design... View All 11 Courses|
|Subjects:||Data Science, UX Design, Digital Marketing, Machine Learning|
Springboard offers self-paced data science courses that can be completed in 2 to 4 months, with one-on-one weekly mentor support. The program costs $499 per month, so students who finish early will pay less for their tuition.