Guide
The 4 Best Machine Learning Certifications for Your Career
Written By Jennifer Inglis
Edited By Jess Feldman
Last updated April 1, 2025
Written By Jennifer Inglis
Edited By Jess Feldman
Course Report strives to create the most trust-worthy content about coding bootcamps. Read more about Course Report’s Editorial Policy and How We Make Money.
Course Report strives to create the most trust-worthy content about coding bootcamps. Read more about Course Report’s Editorial Policy and How We Make Money.
Machine learning is no longer just a futuristic concept – it’s shaping industries, driving innovation, and transforming careers. From fraud detection in finance to self-driving cars in transportation, machine learning (ML) is at the core of cutting-edge technology. As demand for ML expertise skyrockets, a machine learning certification may be the next step to standing out in the job market. We’ve chosen 4 machine learning certifications that can help you prove your skills – choosing the right one depends on career goals, experience level, and industry focus.
At its core, machine learning (ML) is a subgroup of artificial intelligence (AI) that utilizes training in tools and languages such as Python, MySQL, and natural language processing (NLP). Machine learning teaches machines to learn from data, build on previous experiences, recognize patterns and trends, and predict outcomes. When ML apps are provided new or novel data, they can adapt, learn, and grow—similar to the human brain! ML apps use algorithms, rather than a pre-programmed outcome, to handle large amounts of data to identify patterns and learn from them.
And just like the human brain, the more data that ML apps are exposed to, the better they get at learning from examples. Machine learning isn’t new, as it’s been around since the 1950s, with “The Turing Test” (aka the imitation game) and Arthur Samuel, who coined the term “machine learning” and created the first self-learning program and taught a computer to play checkers. Today, ML is used not only for voice recognition and ChatGPT, but in nearly every industry, such as:
|
Industry |
Examples of ML |
|
Manufacturing |
Production process optimization, equipment failure prediction, quality control |
|
Health care |
Drug discovery, disease detection, DNA sequencing, medical image analysis, patient outcome predictions |
|
Finance |
Risk assessment, fraud detection, credit scoring |
|
Retail/e-commerce |
Inventory analysis, customer segmentation, customer behavior analytics, chatbots |
|
Cybersecurity |
Malware analysis, proactive threat detection, enhanced incident response |
|
Transportation |
Route optimization, adaptive cruise control, self-driving vehicles |
As a machine learning engineer at Bloomberg, Flatiron School graduate Matthew works with a small team that “builds internal automation applications for the company... It's very back-end and internal, so there is no real glory, but it’s super nerdy and fun. I specifically do all the machine learning analytics and implementation of predictive modeling, which means I help to predict machine behavior internally.”
It’s no longer a question: machine learning and artificial intelligence are here to stay. In an ever-changing tech landscape, to “future proof” your career it’s worth getting one or more ML certifications. Tangible, real-world benefits to getting a machine learning certification include:
If you’re interested in any of the following careers, ML certification is a must:
| 💡 On-the-ground insight from Senior Solutions Architect at Nvidia & FourthBrain graduate Matt: After finishing FourthBrain’s Data Science Bootcamp and landing his first machine learning job, Matt went on to get his AWS Certified Machine Learning - Speciality certification to elevate his career to a senior-level. |
| 💡 On-the-ground insight from AI/ML Global Black Belt (GBB) at Microsoft & Codesmith graduate Juan: “The fundamental [Azure] certs are not that bad. They lay a great foundation for the fundamentals of Azure: What is this cloud ecosystem? What are some data fundamentals? What are some AI fundamentals? The intermediary certification was more challenging, asking conceptual questions, like: If you were to build this solution, how would you build it? What are the implications? What are the pros? What are the cons? How do these things work? I spent eight months on the job before I looked at the intermediary cert because it was over my head when I first started studying for it. I spent some time on the job and then revisited the certification, which helped me understand things in a way that I wouldn't have without the context of the job.” |
Keep in mind, however, that while these certifications are valuable, if you’re making a career change, it may be more helpful to first enroll in an immersive data science bootcamp instead of studying for individual certifications. Head of Developer Relations (and Springboard graduate) Mikiko points out that “it can be really hard to build a road map for yourself to upskill on your own if you don't know what you don't know. If you don't have a way to build a road map of skills for yourself or you don't have help from someone to build that road map, then I think it can be pretty challenging navigating what's hype and what’s real, in terms of the current AI tooling landscape.”
While most data science bootcamps won’t prepare students for specific machine learning certifications, they will provide a solid foundation for the skills, tools, and programming languages you will need to obtain entry-level certifications. For example, former lawyer Indre enrolled in the Data Science Bootcamp at Turing College, which covers machine learning towards the end of the program. The program helped her land a job as a decision scientist at Vinted!
For those with some experience in data science and/or machine learning or who have graduated from a data science bootcamp, leveling up with a machine learning certificate course may be the next step in your career ladder. For example, the IBM Machine Learning Professional Certificate course offered through Coursera was designed for IT professionals, data scientists, software developers, and business analysts who have a passion for data and basic math, statistics, and programming.
No, certification isn’t absolutely necessary but can be a good way to validate your skillset. Hands-on experience is incredibly valuable, and employers will be looking at your portfolio to determine if your projects are functional, relevant, interesting, and demonstrate your level of proficiency.
Getting a certification can help boost your paycheck, too. According to a 2023 Oxford study, AI/ML skills can provide a salary that’s 21-40% higher than the industry average.
Yes! ML certifications can be very valuable for software engineers, especially if they’re looking to move into the field of AI or land an AI engineer role. Machine learning is a growing field, and certifications will increase a software engineer’s hireability in data science, machine learning, and AI. Obtaining an ML certification can also augment a software engineer’s credibility, validate ML skills, and demonstrate commitment to continuous learning.
Jennifer Inglis, Guest Editor
Jennifer Inglis is a freelance writer, editor, and content creator with extensive professional expertise in advertising, media analysis, teaching, writing, and literature. Prior to becoming a writer, Jennifer was a Media Analyst for ten years and then earned her master's degree in Teaching, instructing middle-school students in college/career readiness, writing, and public speaking..
Jess Feldman, Content Manager at Course Report
Jess Feldman is an accomplished writer and the Content Manager at Course Report, the leading platform for career changers who are exploring coding bootcamps. With a background in writing, teaching, and social media management, Jess plays a pivotal role in helping Course Report readers make informed decisions about their educational journey.
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