Machine learning (ML) and AI specialists are leading the way in transforming the workforce as we know it.
The field will grow by 40%—translating to 1 million new jobs—over the next five years, according to the World Economic Forum’s 2023 Future of Jobs Report. That’s the largest growth of any occupation.
And while many AI and ML jobs simply do not exist yet, learning the in-demand skills may end up having a sizable return on investment. Many businesses across a variety of industries are spending more on AI—from Papa John’s to Canva—thus translating to a need for workers to have relevant skillsets such as knowing natural language processing, prompt engineering, and Python.
Luckily, there are a growing number of ways to learn these skills. One broad example is online learning platform Udemy, which hosts dozens of offerings across a wide spectrum of experience level, length, and price. Many universities and tech firms also have paid and unpaid classes and certification programs that you can complete from the comfort of your own home.
But if you’re looking for a little bit more structure in your learning experience, a bootcamp program may be a good avenue to check out. They are often shorter, cheaper, and more flexible than a traditional degree program but may provide more substantial learning than one course or a shorter certificate. Plus, some bootcamps importantly provide career services and mentorship opportunities—guiding students through the entire job search process, from networking all the way to salary negotiations.
Earlier this year, Springboard launched its machine learning and AI bootcamp in partnership with three universities—highlighting just how new and growing the subject is in education.
Kara Sasse, chief product officer at Springboard, says the bootcamps are catered to fit the needs of those working professionals who are eager to upskill and succeed in increasingly AI-focused job environments.
“As ML and AI continue to transform every aspect of our lives, forward-thinking organizations must actively take inventory of potential skills gaps and look for professionals with the tools to succeed in this evolving landscape,” Sasse tells Fortune.
Springboard’s courses feature hands-on projects and practical exercises, which will boost students’ technical skills in areas like data preprocessing and feature engineering as well as soft skills like problem-solving and strategic thinking, Sasse adds.
Fullstack Academy similarly offers an AI and ML bootcamp that covers fundamentals as well as emphasizes practical application, according to the company’s CEO, Nelis Parts. The roughly 6-month long program concludes with a career-simulated project.
With business leaders planning to invest heavily in AI in 2024 alone, it is important than ever to enable individuals from diverse professional backgrounds to learn in-demand AI and ML skills, Parts notes—helping to in part bridge the tech skills talent gap.
“The rapid and widespread adoption of AI and machine learning technologies has ushered in a profound transformation across various industries,” Parts tells Fortune. “As a result, AI and machine learning engineering roles have become both lucrative and exceptionally high in demand.”
Both Springboard and Fullstack Academy boast that their bootcamp graduates have gone on to be hired at companies of all sizes and industries—notably including top tech companies like Amazon and Google.
But these two programs are just examples of the many opportunities available in the ML and AL space. Below you’ll find a few of the bootcamp offerings on the market. Program lengths average anywhere from eight weeks to nine months, and prices range from as low as a few thousand to nearly $15,000.
Partners: Arizona State, Columbia, Michigan State, Ohio State, Southern Methodist, University of Central Florida, UNC Charlotte, University of Denver, University of Kansas, University of New Hampshire, University of Richmond, University of Utah
Length: 8–10 weeks
Prerequisites: Proficiency in Python and mathematics, including linear algebra, statistics, and multivariable calculus; work experience recommended
Topic examples: Machine Learning Optimization; Neural Networks and Deep Learning; Natural Language Processing
Length: 6 months
Prerequisites: Recommended experience in coding, post-secondary math, and/or 3–5 years employment in a highly computational field
Topic examples: Applied Data Science with Python; Deep Learning; Generative AI & Prompt Engineering
Partners: Caltech and UT-Dallas
Cost: $10,000 (Caltech), $8,000 (UT-Dallas)
Length: 6 months
Prerequisites: Experience in programming and mathematics; at least two years of formal work experience preferred
Topic examples: Deep Learning with Keras and TensorFlow; Applied Data Science with Python; Essentials of Generative AI, Prompt Engineering, and ChatGPT
Partners: UC San Diego Extended Studies, University of Maryland Global Campus, UMass Global
Length: ~9 months (15 to 20 hours/week)
Topic examples: Machine Learning Models; Deep Learning; Ethics and Bias in Machine Learning