Guide to Online Machine Learning Certificate Programs

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, less intensive, less expensive, and geared toward different audiences.

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.

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.

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.

Professionals at any stage of their careers have varying motivations for attending non-bootcamp machine learning certificate programs. 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.

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.

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 beyond business analytics, including medical diagnosis, high-frequency trading, image recognition, product recommendation, and much more.

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.

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.

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.

Best for upskilling

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.

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.

Best for management

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.

Best machine learning bootcamps

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.

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.

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.