The proliferation of data-driven decision-making in almost every industry is showing no sign of abating, and the workforce will need to keep up. “By 2025,” according to one McKinsey report, “[n]early all employees naturally and regularly leverage data to support their work.”
For many, this will mean engaging with the machine learning models that are increasingly driving business growth: a different McKinsey report notes that upwards of 50% of organizations polled in 2022 were employing artificial intelligence technologies, twice as many as in 2017.
Many who don’t know where to start with machine learning are increasingly looking to online machine learning courses in order to set themselves on a new career path or move down one they’ve already started. But to extract the maximum benefit, not just any machine learning course will do. There’s significant variation in not just what is taught, but how and to what ends.
In this guide, we’ll explain why you should consider taking a machine learning course online, what you can expect from one, and how you can choose the best one for your unique situation.
The state of online learning
If you’re considering taking a machine learning course online, you’re in good company. Between the embrace of remote-first education during the COVID-19 pandemic and employees’ increasing willingness to seek out upskilling opportunities that will allow them to move more quickly between jobs and industries, online courses have never been more popular. According to Statista.com, almost 25,000 learners graduated from North American bootcamps in 2020, over ten times as many as in 2013. Globally, HolonIQ estimates over $10 billion was spent on micro- and alternative credentials in 2021, with this spending potentially doubling over the next 3-5 years.
For someone who wants to learn more about machine learning, the rise of online learning is already paying dividends: there have never been more opportunities to learn the difference between a neural network and a support vector machine from the comfort of your couch.
But couch-based learning is just one benefit of online learning — online learners choose to learn online for a variety of other reasons, including:
Online machine learning courses are flexible not only because you can complete them from home, but because often you can complete them on your schedule. While some courses hold live sessions that students are required to attend at a specific time, many offer pre-recorded learning materials that students can consume whenever works for them. This means that you can add machine learning skills and expertise to your arsenal while still working, taking care of a loved one, or going to school for something else.
Recently, LinkedIn CEO Ryan Roslansky suggested that companies care more about skills than degrees when making hiring decisions. The skills vs. degree debate is still ongoing, and Roslansky is hardly impartial given LinkedIn’s LinkedIn Learning offerings. Still, there’s no denying that machine learning requires a (*ahem*) very particular set of skills that, while they can be learned in degree programs, can also be learned through short courses that often cost pennies on the dollar compared to degree programs, if they cost anything at all.
For many, there are just too many financial, geographic, and academic barriers to learning from field-leading experts at the world’s most prestigious institutions. Online courses work to break down these barriers, letting people all over the globe access courses from Columbia, Harvard, or the University of Michigan without ever setting foot in New York City, Cambridge, or Ann Arbor.
Those who want to build machine learning skills, especially those who are already in the workforce, often don’t have time to pursue a four-year undergraduate or two-year graduate degree, especially if these degrees will require them to take courses that don’t contribute to their goals. In just a couple of hours or a couple of weeks, machine learning courses allow these individuals to learn the skills they need so that they can start applying them in the real world.
Though almost all can benefit from the flexibility, affordability, accessibility, and efficiency offered by machine learning courses, that doesn’t mean they all seek out machine learning courses for the same reason. We’ll dive into these various reasons next.
Why should you take a machine learning course?
While some attend online machine learning courses with the hopes of becoming a data scientist or machine learning engineer, not all do: some are just looking to explore potential careers, add skills to level up in their current job or manage machine learning teams and operations. Read on to see if one of these use cases matches your situation.
To explore a career in machine learning or data science
Online machine learning courses — especially self-paced free online courses from LinkedIn Learning, Coursera, Udemy, DataCamp, and Udacity — are great ways to see if a career in machine learning or data science would be right for you. By gaining exposure to the basic concepts and even some of the technical skills required for machine learning, you can start to get a better idea of the day-to-day of a machine learning engineer or data scientist and gauge whether you would be successful and happy in one of these roles.
To add a skill set to boost job performance
Machine learning courses don’t just benefit future machine learning engineers or data scientists: for program managers, data analysts, or IT professionals, they can be a great way to add programming and fundamental machine learning skills that can boost performance on the job and future-proof employment status for years to come.
To manage ML teams and operations
Increasingly, colleges and universities are offering professional certifications in machine learning geared towards mid- and upper-level managers and C-suite executives who want to gain foundational knowledge of machine learning to implement and oversee machine learning operations at their companies. These courses usually focus less on hard machine-learning skills and more on case studies of machine-learning applications in business areas like operations, human resources, team-building, and strategy.
To begin a new career in machine learning or data science
Machine learning courses are also frequently sought out by aspiring machine learning engineers and data scientists looking for a way to break into these respective fields. With such promising career prospects, this should come as no surprise. Data science remains “the sexiest job of the 21st century,” even 10 years after the Harvard Business Review first awarded the title, and the machine learning engineer job market is similarly strong, with postings growing by nearly 350% between 2015 and 2018 according to Indeed and showing no sign of slowing down. (For more on the distinction between the two, see our deep dive.)
Individuals seeking these positions usually gravitate towards more expensive and intensive machine learning and data science bootcamps, though it is certainly possible to land an entry-level position after only taking a free machine learning course if you have a stellar portfolio of work…and perhaps some connections within the company. Either way, if you want a job in machine learning or data science, a machine learning course could be a good investment: machine learning engineers earn an average annual salary of $120,883 in the US, while data scientists earn $139,202, both well over the average annual salary in the US of $58,260.
What can you expect to learn in an online machine learning course?
Though there will certainly be variation — especially for courses with a specific focus on a skill or industry application — if you choose to take a machine learning course you can generally expect to cover some or all of the following:
Machine learning theory
Though short courses generally deemphasize machine learning theory compared to full-fledged degree programs, many include at least some background in machine learning and artificial intelligence foundations. That said, theoretical content is usually directly connected to ML applications rather than harping on philosophical distinctions related to knowledge representation and logic.
At its core, machine learning is just creatively applied mathematics, in particular statistics, probability, and linear algebra. Some courses will only be open to those who already have a background in these kinds of mathematics, while others will provide training in them either through pre-course materials or through units embedded in the course curriculum.
The ability to write code in a programming language like Python or R is crucial for someone who wants to write machine learning algorithms (the fundamental bits of code that allow computers to learn). Like with mathematics, courses will either require baseline skills as a prerequisite or help students develop them either before the course or during the course. Many machine learning courses also introduce students to the many machine learning software libraries like PyTorch, TensorFlow, Pandas, and Keras, especially if students will be building more advanced deep learning models using a convolutional neural network or generative adversarial network.
Machine learning techniques
Did we lose you at “convolutional neural network or generative adversarial network?” Don’t worry, most online machine learning courses will also provide theoretical and practical instruction in the most popular machine learning techniques, starting with unsupervised learning, supervised learning, and reinforcement learning, and often diving into deep learning and neural networks.
Machine learning applications
Many courses will also cover the most popular and impactful machine learning applications and use cases, including computer vision and natural language processing, as well as applications for data analytics. For courses more geared towards those looking to become machine learning professionals, these courses will provide practical instruction in which machine learning algorithm or machine learning model should be used in which situation, while courses geared more towards managers and executives will provide a bird’s-eye view that emphasizes business impact.
Machine learning strategy
These executive-focused courses will also include training in how to design an effective machine learning strategy, including units on ML governance, team-building, and enterprise deployment practices. Courses focused on ML skills will usually omit this training.
What kinds of online machine learning courses are out there?
If you think a machine learning course will be useful to your career and are excited about the curriculum, you still need to figure out what kind of machine learning course would be right for you. Machine learning courses generally fall into one of four categories: certificate programs, massive open online courses (MOOCs), and cohort-based courses (CBCs), though there is substantial overlap between these.
Machine learning certificate programs can be self-paced or live, and in most cases will take place online. They can vary in their intended uses and curricula: some teach machine learning skills, while others provide training in how to implement machine learning solutions in enterprise. They also vary in their cost: some certificates can be earned for free, others (like those offered by Coursera and Udemy) require a monthly or annual subscription, while others require tuition ranging from a couple hundred dollars to several thousand. Upon completion, student earn a certificate that they can add to their LinkedIn or resume.
To learn more, see our machine learning certificate guide.
Massive open online courses
Machine learning massive open online courses are free or low-cost open-access courses that can support thousands of participants at any one time. ML MOOCs can have a variety of different foci, from discrete skills to comprehensive machine learning training, to applied machine learning practices, through asynchronous materials such as pre-recorded videos, readings, and problem sets. Sometimes, students have the opportunity to interact through digital touchpoints such as a message board, chat, or discord.
Machine learning cohort-based courses, an alternative to MOOCs quickly growing in popularity, promise more learner engagement by offering smaller class sizes, more interaction with faculty and peers, and more emphasis on skill practice and feedback. As with MOOCs, machine learning CBCs can serve many different purposes for learners at all stages of their careers. Because of the higher level of interaction with instructors and peers, smaller class sizes, and synchronous study, these are generally more expensive than their massive, asynchronous alternatives.
A Longer-Form Option: Machine Learning Bootcamps
While we won’t go too far into them here, a longer, more intensive option for someone looking to land an entry-level machine learning or data science position is a machine learning bootcamp. In exchange for higher tuition — the average bootcamp cost $11,727 in 2020 — students receive a comprehensive machine learning education and extensive career services.
To learn more about machine learning bootcamps, check out our extensive guide.
What other factors should you consider when researching online machine learning courses?
Even within their distinct types, not all machine learning courses are created equal: they differ in curriculum, modality, reputation, and cost. There is no one recipe for how to weigh these in your decision-making process: ultimately, you will have to prioritize what is most important to you and try to find the program that best matches your needs. As you do, here are some things to keep in mind.
We’ve already previewed what you could expect to learn in a generic machine learning course, but as you research different courses, you’ll want to make sure to pay attention to any variation. You will be paying for everything you learn — or in the case of free courses, spending the time to learn it — so you want to make sure that what the course teaches aligns with what you need to learn.
Something else to focus on when searching for a machine learning online course is the modality of that course: how learning takes place. You want to find something that fits your learning preferences while also being mindful of cost.
Self-paced online courses offer pre-recorded learning content that can be accessed asynchronously, so attendees have the flexibility to study when it suits them and progress on their schedule. While some self-paced online courses are open-ended, meaning that students can take as long as they want to complete the curriculum, others have concrete start- and end-dates, potentially aligned with exams. Some also offer one-on-one mentoring opportunities in addition to the course content, which can happen live through video chat.
Because they offer asynchronous learning, self-paced online courses are a great option if someone wants to study part-time, either because they wish to continue working or have familial obligations. Often, self-paced courses are also less expensive due to the lower cost (and higher scalability) of offering pre-recorded materials.
Instead of pre-recorded videos, live online courses feature real-time instruction from faculty through a video conferencing service. To encourage student-teacher and student-student interaction, these courses are often cohort-based and capped at a relatively low number of students, usually around 35.
Because instruction is synchronous, there are sometimes opportunities for group projects and live feedback on independent capstone projects. Live online courses can be offered both part-time and full-time, but because students learn in real time, they will have to pick an option at the outset and stick with it. Because live instruction requires instructors to be scheduled and consistently compensated for their time, scalability remains low and costs relatively high, with this passed on to the consumer.
We aren’t proponents of academic snobbery, but we still think it’s important to pay attention to a course’s reputation — and the reputation of the institution offering it — for two reasons. First, in many cases, a more reputable educational provider will attract more expert faculty and more driven students, both of which can improve your learning experience. Second, if you earn a credential from a program and want to display it on your resume or LinkedIn profile, it will serve you best if it’s from a prestigious institution.
That said, many academic institutions are outsourcing online course instruction and design to third parties these days. This doesn’t necessarily mean that the resulting courses are of lower quality, but when researching programs, make sure you understand who exactly will be teaching you and what impact this might have on your learning.
You also want to be aware of the premium that comes with prestige: because elite universities are globally known brands, they can demand higher tuition than other, just as worthy institutions. Ultimately, you might have to decide whether the boost in name recognition is worth the surcharge when you can get the same education elsewhere for cheaper — which leads us to our final factor, cost.
As we’ve shown above, if you want to learn machine learning online for free, you can: there are plenty of self-paced, asynchronous, free online courses and other resources that can help you build skills and expertise in the field.
But learning this way isn’t always easy: it requires discipline and the ability to teach yourself in a vacuum. Some just don’t learn this way or learn more efficiently and effectively in a more structured environment where they receive feedback from an instructor and can learn from their peers. More often than not, this requires paying at least some tuition. Courses like this can range from several hundred dollars to several thousand.
Paying tuition brings with it other advantages, such as the prestige of selective credentials, career services like resume help, and a motivating feeling of investment that causes many to commit more fully to their plan of study. Ultimately, you will have to determine for yourself whether these advantages are worth it, and thus how the cost will factor into your decision-making process.
Our picks for the best machine learning course online
By now it should be evident that we aren’t interested in ranking online machine learning courses — there is simply too much variation both in the needs of students and in available offerings. For this reason, we’ve decided to instead provide high-quality options that span the kinds of machine learning courses and various use cases out there, taking into consideration as we do the factors we’ve mentioned: curriculum, modality, reputation, and cost. We’ve strived to provide options at all price-points, but we’ve capped our recommendations at $5,000. For more expensive — and longer form — options, see our guides to machine learning certificates and machine learning bootcamps. And while we’ve tried to represent all price-points, we haven’t tried to be comprehensive: the below list is intended to help you kick of your research and find the course that works best for you.
Best Machine Learning Courses for Career Exploration
LinkedIn’s Getting Started with AI and Machine Learning
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)
Google’s Machine Learning Crash Course
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 pre works 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 Skills-Based Machine Learning Certificate Programs
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 Machine Learning Courses for Managers and Executives
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
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
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.