Today, twice as many businesses are employing artificial intelligence technologies than in 2017 — specifically over 50% of organizations polled in a recent McKinsey survey. By and large, these AI technologies are being powered by machine learning, the most popular subset of machine learning that relies on algorithms imbued with the ability to continually improve their capabilities.
This boom in machine learning shows no sign of stopping, which explains why more and more people are choosing to earn a machine learning certificate online to gain crucial context and skills to stay relevant in a changing professional landscape.
The state of online certificate programs
Understandably, the increasing demand for machine learning certificates is keeping pace with the rise of the online certificate program across all disciplines. Over the past decade, certificate programs and other short courses exploded in popularity, especially for those looking to enter technical fields out of college or switch careers without spending time and money on a traditional four-year degree program. In fact, according to Statista.com, almost 25,000 learners graduated from North American bootcamps in 2020, over ten times as many as in 2013.
On the heels of the Great Resignation and the proliferation of online learning offers in the wake of the COVID-19 pandemic, it’s likely that these graduation figures will continue to rise.
Is a machine learning certificate right for you?
Should you join the ranks of online learners by investing in a machine learning certificate? In this guide, we’ll dive deeper into these programs to give you a better idea of what to expect and help you better understand how you can leverage an ML certificate to get ahead. At the end, we’ll present our recommendations for the best programs out there for a variety of use cases.
What is machine learning?
If you’re just beginning to research how machine learning might help you move forward in your career — perhaps a new career path, a better job, or skills and context to help you perform better in your current position — you might still be confused about what exactly machine learning is.
The most widely used subdiscipline of artificial intelligence, machine learning centers around algorithms that enable computers to learn over time to complete complex tasks and data analysis. As Stuart Russell and Peter Norvig describe the process in Artificial Intelligence: A Modern Approach, the standard textbook for beginning AI and ML students; “a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems.”
Often, this model-building relies on “training data,” relevant input data for which the output is already known, in what is called “supervised learning.” If you’ve identified buses, stop signs, or palm trees in a reCAPTCHA while filling out a form, you’ve contributed training data for the supervised learning behind self-driving cars. In “unsupervised learning,” on the other hand, the machine learning algorithms build a model without any training data by discovering hidden patterns and other insights in data sets. If you’ve ever put a related item in your cart that “Customers also bought” while shopping on Amazon, you’ve benefited from unsupervised learning algorithms that detected patterns in consumers’ buying habits.
What does a machine learning certificate program entail?
A machine learning certificate program trains participants in certain ML skills and concepts over the course of several months, with participants who successfully complete the curricula awarded a certificate at the program’s close.
Machine learning certificate programs differ from other professional certifications because they are decentralized and unique to the educational providers offering them.
This distinguishes someone holding an ML certificate from a board-certified doctor, a certified financial advisor (CFA), a bar-certified lawyer, or a certified public accountant (CPA). These latter certifications are centrally administered and carry legal and professional weight. A machine learning certificate, by contrast, only signals to potential employers that an individual has completed a course of study.
Certificate program or bootcamp?
There’s lots of overlap between certificate programs and bootcamps. Our rule of thumb is that all bootcamps are certificate programs — you do receive a certificate for completing one, after all — but not all certificate programs are bootcamps. Non-bootcamp certificate programs are generally:
Shorter: Non-bootcamp ML certificate programs usually only last a couple months of around 4–5 hours of study per week. Bootcamps, on the other hand, usually last three or more months of 20+ hours of instruction per week.
Less intensive: Because there is less time in the (virtual) classroom, non-bootcamp ML certificate programs don’t go into the same detail as a bootcamp would.
Less expensive: A non-bootcamp ML certificate program is typically less expensive than a bootcamp. While bootcamps averaged $11,727 in 2020, ML certificate programs are usually less than $10,000, and often much less. There are, of course, notable exceptions, such as MIT Professional Education’s Professional Certificate Program in Machine Learning & Artificial Intelligence.
Geared towards different audiences: Professionals at any stage of their careers have varying motivations for attending non-bootcamp machine learning certificate programs — in fact, many of these programs have specific uses (see below). Machine learning bootcamps, however, are usually geared towards someone who wants to land an entry-level job as a machine learning engineer or data scientist. If that sounds like you, you can dive deeper with our bootcamp guide.
What are some use cases for machine learning certificate programs?
Exploring a career in machine learning
For anyone curious about machine learning but not ready to dive into an expensive and time-intensive bootcamp or degree program, the affordability and convenience of a non-bootcamp certificate program can be appealing. While it likely won’t let you jump into a senior artificial intelligence engineer, machine learning engineer, or data scientist role right away, it can give you a broad overview of key machine learning skills and concepts, as well as a glimpse into the day-to-day existence of a machine learning professional.
Adding a skill set to boost job performance
For someone working as a product manager, data analyst, or in information technology (IT), a non-bootcamp certificate program can be a great way to add skills that can help them take the next step in their jobs without sacrificing the time and money to take a bootcamp or go to graduate school.
Managing ML operations
Sometimes machine learning certificate programs aren’t focused on building machine learning models so much as managing machine learning operations. Many programs offered by business schools, for example, teach C-suite level executives and other senior managers the basics of machine learning so that these leaders can more successfully oversee the implementation of ML solutions in their businesses.
What skills and concepts are taught in ML certificate programs?
As noted above, the curricula of ML certificate programs can differ based on who they are geared towards. That said, in general these programs cover some or all of the following topics to at least some extent:
Applied mathematics, including statistics, probability, and linear algebra
Programming languages, most frequently Python and its associated machine learning software libraries (e.g. PyTorch and TensorFlow), though the R programming language may also be taught in programs that emphasize data science
Machine learning techniques, including supervised learning, unsupervised learning, deep learning, neural networks, etc.
Machine learning applications, including tools, techniques, and algorithms for use with big data in industries like healthcare, finance, and manufacturing
ML strategy, including ML governance, ML team-building, and enterprise deployment practices
Related AI subdisciplines, such as computer vision, natural language processing, and robotic process automation
While there are topics (above) specifically geared toward ML certificates, many candidates exploring the field also research data science. Let’s discuss the difference.
Machine learning or data science?
When considering a machine learning certificate program, it makes sense to also entertain options in data science. Data science focuses on leveraging complex data analysis — often with the help of machine learning — to develop actionable insights to support business goals.
These days, data science programs are so ubiquitous that it would be easy to think of machine learning as just a tool for data science, but this wouldn’t do justice to machine learning. While it certainly does have utility for data science, machine learning algorithms have important applications mere business analytics, including medical diagnosis, high-frequency trading, image recognition, product recommendation, and much more.
So what’s the upshot for someone considering a certificate program in data science or machine learning?
If you’re driven by data and want to focus on producing novel new ways to analyze it and develop insights that can directly support business decisions — or you want to manage these operations — a data science certificate program or bootcamp would be a good choice.
If instead you’re more interested in programming and building advanced machine learning models from the ground up that may support the work of a data scientist or may have applications in a number of different consumer and commercial products — or, again, you want to build expertise to support machine learning operations — you’d be best served by a machine learning certificate or bootcamp.
What to look for in a machine learning certificate program?
While investing in a machine learning certificate program is less of a financial risk than investing in a bootcamp or degree program, you still want to make sure you get your money’s worth. So how do you know that a program will be worth your while? Here are some factors to consider:
Whether or not a program has the right curriculum for you will depend on how exactly you plan to use what you learn. If you are looking to learn more because you want to hire machine learning engineers or data scientists to streamline your company’s operations, then you don’t want to pick a program that will painstakingly teach you Python from the ground up. If you want to determine if a machine learning career is right for you, however, then it would be a good idea to choose a program that will have you doing a considerable amount of coding, as this would simulate the work you would do as a machine learning engineer.
Even if their curricula may be the same, often the modalities of online ML certificate programs differ. Some are completely self-paced with pre-recorded instruction, while others offer live instruction. Still others offer a hybrid of synchronous and asynchronous learning. Generally, asynchronous courses will be less expensive than those that offer facetime with an instructor and classmates. It’s up to you to determine if the opportunity to engage with the instructor and your fellow students is sufficiently valuable to warrant the premium.
Since machine learning certificate programs are decentralized, your certificate is only as good as the institution granting it. Accordingly, it’s important to take reputation into consideration when researching programs. There’s no guarantee that having a name like Stanford University on your resume or LinkedIn profile will help you get a job when you’ve only completed a certificate, but it certainly can’t hurt.
Many also flock to programs offered by famous educational institutions in the belief that the level of instruction will be higher. This is sometimes the case, but not always: increasingly, institutions are partnering with online program managers for their online offerings. While this doesn’t necessarily mean that these programs are of lower quality, it’s in your best interest when considering a program to know exactly who you will be teaching you and what their credentials are.
As we said, a non-bootcamp certificate program is less of a financial risk than a bootcamp or degree program — but it’s still a risk. When researching programs, compare what they have to offer in terms of curriculum, modality, and reputation against what else is out there, and in particular against free programs. You might find that what one educational provider is offering can be obtained without cost elsewhere.
Our picks for best machine learning certificate programs
What’s our approach?
In coming up with our picks for best machine learning certificate programs, we balanced the above factors (curriculum, modality, reputation, cost) and sought the best programs for each use case outlined (career exploration, upskilling, management).
Best for career exploration
If you’re considering embarking on a new career path, there’s a good chance you already spend a lot of time on LinkedIn, which is all the more reason to take advantage of LinkedIn Learning’s Getting Started with AI and Machine Learning learning path. Accessible even for the technologically un-savvy, LinkedIn’s course offers learners an introduction to the basics of machine learning, examines how businesses are deploying machine learning solutions, and previews the Python programming language and its use for machine learning and natural language processing.
With only 20 hours of online, self-paced study, someone considering a career in machine learning can assess whether it's for them within a week, then choose to dive deeper or move on in their search.
Duration: 20 hours
Modality: Online, self-paced
Cost: Included with LinkedIn Learning membership ($29.99)
For someone looking for a more hands-on exploration of machine learning, Google AI’s Machine Learning Crash Course is a great option. In just 25 lessons and 15 hours, learners sprint through the basics of machine learning using real-world case studies and practice exercises using Google’s TensorFlow APIs. For students without any prior machine learning or programming experience, Google’s free online course provides preworks to complete to get up to speed on NumPy and Pandas software libraries and the basics of machine learning problem framing.
Duration: 15 hours
Modality: Online, self-paced
Best for upskilling
DeepLearning.AI and Stanford University’s Machine Learning Specialization
DeepLearning.AI and Stanford University’s Machine Learning Specialization offer fundamental training in machine learning techniques like supervised learning, unsupervised learning, and reinforcement learning, as well as hands-on training in how to develop machine learning models using the Python Programming language and a neural network with the TensorFlow software library.
Hosted on Coursera and taught by DeepLearning.AI founder Andrew Ng, all three courses that comprise this certification (“Supervised Machine Learning: Regression and Classification,” “Advanced Learning Algorithms,” and “Unsupervised Learning, Recommenders, Reinforcement Learning”) can be audited for free, though a subscription is required to earn a certificate of completion.
Duration: 3 months, 9 hours per week
Modality: Online, self-paced
Cost: Free to audit; Coursera subscription required ($49/month) required for the certificate
University of Washington’s Certificate in Machine Learning
Designed for software programmers, statisticians, and other STEM professionals who want to build a machine learning skill set, the University of Washington’s Certificate in Machine Learning offers a robust nights-only curriculum covering necessary mathematics and programming skills, advanced machine learning concepts necessary for applications like natural language processing, recommendation systems, and forecasting, and deep learning techniques like reinforcement learning and generative adversarial networks (GANs).
While UW’s course is more expensive than DeepLearning.AI and Stanford’s, it offers more opportunities for interaction. Offered online, students meet once weekly for 3 hours to stream course content together and communicate in real-time using web conferencing and chat. According to UW, 87% of course alumni are working in the field at companies like Microsoft, Amazon, Boeing, and Expedia.
Duration: 8 months, 9 hours a week
Modality: Live online with digital touchpoints
Best for management
MIT Professional Education’s Professional Certificate Program in Machine Learning & Artificial Intelligence
MIT’s Professional Certificate Program in Machine Learning & Artificial Intelligence offers a host of courses that together can equip the C-suite and other senior executives and managers with the tools and know-how to implement machine learning solutions to revolutionize their benefits. Participants have the option to study on campus, but most of the curriculum is offered through live online classes. The curriculum includes two core classes exploring how to utilize machine learning techniques to process big data to drive growth in different fields and electives such as “AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment,” “Ethics of AI: Safeguarding Humanity,” “Machine Learning for Healthcare,” and “No Code Analytics and AI.”
To receive the certificate, students must take 16 days of classes over 32 months, with each day costing roughly $1000. Accordingly, we recommend this course for mid and late-career executives, especially if they have professional development funding available to them. That said, electives like “Reinforcement Learning,” “Deep Learning for AI and Computer Vision,” and “Bioprocess Data Analytics and Machine Learning” would be instructive for early- to mid-career data scientists, machine learning engineers, and software engineers.
Duration: 16 days within a 32-month period
Modality: On-campus or live online
Cost: Approx. $16,000
Best Machine Learning Bootcamps
BerkeleyExecEd’s Artificial Intelligence: Business Strategies and Applications
Artificial Intelligence: Business Strategies and Applications, offered through a partnership of UC Berkeley Executive Education and Emeritus, is a more affordable, less time-intensive option for senior executives looking to gain an understanding of the basics of machine learning, including deep learning and neural networks, as well as applications in computer vision, natural language processing, and robotics. Students also take several modules specific to implementing machine learning solutions within organizations, including “AI Strategy,” “AI and Organizations: Building Your AI Team,” and “The Future of AI in Business.”
In addition to C-suite and other senior executives, this program can help mid-level professionals working in data science and business analytics begin building out their AI skill sets. Alumni can take advantage of a host of benefits including local networking events and an annual conference.
Duration: 2 months, 4-6 hours per week
Modality: Online with live teaching sessions
Caltech’s AI & Machine Learning Bootcamp
Caltech’s AI & Machine Learning Bootcamp, offered in collaboration with IBM through a partnership with Simplilearn, provides learners who have previous experience in programming and mathematics with comprehensive training in artificial intelligence, machine learning, and deep learning using the Python programming language, with applications in data science and computer vision. The program culminates in a capstone project guided by dedicated mentoring sessions.
Students also have the opportunity to attend master classes with Caltech professors and IBM professionals, participate in IBM hackathons, and go on a virtual tour of Caltech’s Robotics Lab. In addition to the curriculum, students receive career services from Simplilearn, including one-on-one interview training and resume-building assistance.
Duration: 6 months, 5-10 hours per week
Modality: Online, live, and self-paced hybrid
UC San Diego’s Machine Learning Engineering Bootcamp
UCSD’s Machine Learning Engineering Bootcamp, powered by Springboard, features training in deep learning and computer vision and image processing, with an emphasis on deploying machine learning models at scale. As with Caltech’s bootcamp, UCSD also culminates in an end-to-end capstone project that students can use to start their machine learning portfolio. Students are required to have prior experience in computer science and/or applied mathematics.
Throughout the bootcamp, students are supported by one-on-one calls with a dedicated mentor working at an industry-leading company and an online community in which students can interact and learn from one another. Students also receive one-on-one career coaching covering informational interviewing, resume creation, and interviewing.
Duration: 6 months, 15 hours per week
Modality: Online, live, and self-paced hybrid
Enrolling in and completing machine learning courses online can be a convenient and effective way to improve your skills and potentially shift your career direction. These courses offer a flexible learning format, enabling you to learn at your own pace and fit in your education around your schedule. The need for machine learning professionals is increasing, and those with the skills and knowledge gained from these courses may be able to earn higher salaries in the job market. With a variety of options available, it's crucial to research and find the course that best meets your needs and goals.