Machine learning is one of the fields on the cutting edge of technology today: how to program computers to look for patterns in data and make predictions – or even take actions – based on these patterns. So it’s little surprise that it’s one of the hottest topics to learn about… and not just on college and university campuses around the world.
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Open online course platforms, like Udacity and Coursera, are getting in on the teaching action, too. But they don’t necessarily teach machine learning the same way. So what are you in for – and what can you get – from the courses on Udacity about machine learning vs. Coursera’s comparable lessons? We’ll give you a general overview in this article.
What is covered in this article
We’ll start with some quick facts and insights regarding how machine learning is taught on the two websites, then have a deeper look into a few of the key general facets of each platform.
Udacity vs. Coursera: machine learning comparison table
Generally, Udacity has fewer machine learning courses than Coursera. But Udacity’s approach is more focused and fast-paced, designed to get you into the machine learning industry within 4 to 7 months. Coursera’s comparable programs generally take a bit longer to complete, and have more of an academic-oriented approach than a business-oriented one. However, they are much more accessible and affordable.
|Best Uses||Those wanting all the tools they need to jump right into a career with a (leading) machine learning company||Those looking to take a more theoretical/academic approach to studying machine learning, or just spend less money|
Comparing Udacity vs. Coursera machine learning courses
Now we’ll dive into how Udacity and Coursera perform in teaching machine learning when it comes to four key tenants: how much material they offer, what it costs to take a program, their general overall teaching styles, and what you can expect to earn from completing a program.
Volume of Courses
Exactly how much about machine learning is available to learn about on Udacity and Coursera? This section will look at the number of machine learning programs each offers.
Udacity’s a pretty small site, so it goes for quality over quantity. It has 14 courses related to machine learning, only 5 of which actually have “machine learning” in the title (and one is a career development course on how to interview for a machine learning job). Still, Udacity crams a lot of content into the courses it does have, especially the Nanodegrees. You’ll have to work harder and faster to complete them, but you’ll learn about as much as you would in a Coursera Specialization or Professional Certificate program, and in less time, too.
A noticeable flaw of Udacity’s library of machine learning courses (and their library of courses in general) is that it’s only available in English. So if you’re not that comfortable with that language, you’re going to want to try Coursera or another option.
Coursera has a much larger overall course library than Udacity does, and that extends to courses on machine learning as well. There are over 130 courses on Coursera that involve machine learning as a skill, over 40 of which actually have “machine learning” in the title. However, those numbers can be a little misleading, as a handful of those courses are actually duplicates that are designed for learners who use specific languages (like Brazilian Portuguese). Still, multilingualism is a feature that Coursera offers while Udacity does not.
Coursera also has over 45 guided projects, 6 Professional Certificate programs, and 3 online Master’s degree programs related to machine learning. So if you want to go wide and study machine learning from a number of different angles, then Coursera has you covered. But some people can be overwhelmed by the number of choices, or just not have the time. In that case, Udacity might be the better pick.
Learning online is often a lot cheaper than doing it in the actual halls of academia, but it can still cost you. Let’s see how Udacity and Coursera stack up in that regard.
Udacity’s courses on machine learning are split roughly 50-50 between free-to-take ones and the pay-to-take Nanodegrees. Each Nanodegree has a suggested timeframe for completing it, usually about 3 to 4 months. You can purchase access to the Nanodegree course for this amount of time at a reduced cost, or pay for each month of access individually. You can switch to the latter system if you buy access for the suggested timeframe, but end up needing more time to finish the course.
In either case, Nanodegrees are still pretty expensive. Purchasing access for the course’s suggested timeframe costs $280/month, while paying on a month-to-month basis will cost you $400/month.
You can take certain individual courses on Coursera for free, or choose to take them for a cost and get a certificate of completion if you pass. Specializations and Professional Certificate programs are generally at the same price rates, but are paid for on a per-month access basis as opposed to a per-course basis (i.e. $50/month as opposed to a one-time $50 fee).
A somewhat frustrating thing that we found is that certain Coursera courses cannot be taken on their own, and must be taken as part of a Specialization or Professional Certificate program. And perhaps because Coursera’s programs are presented by different institutions and companies – unlike the centralized approach Udacity takes – their prices can fluctuate quite a bit. One course may cost $40, another $50, and another $80! And remember that you pay these rates monthly if you’re in a Specialization or Professional Certificate program.
Still, unless you’re taking a full online Master’s degree related to machine learning (at $22,000 – $26,000!), paying for Coursera’s programs at these rates will almost always cost less than taking a Udacity Nanodegree.
It is being increasingly recognized in education that how something is taught is almost as important as what is actually taught, since this affects how well students remember the lesson. Coursera and Udemy have distinctly different teaching methods, as we will explain.
Udacity takes a project-based approach to teaching; it has been likened to a skilled trades school in contrast to Coursera’s more academic bent. Nanodegrees usually contain around 5 or 6 projects designed by tech industry veterans, and the learning materials are centered around teaching you the skills needed to complete them. This can all be done more-or-less at your own pace; in fact, the quicker you finish a program, the less it costs you!
One of the things that sets Udacity apart is its mentorship program. People can apply to be mentors on Udacity, and Udacity will train them in how to evaluate students’ assignments and offer on-demand technical learning support. This makes things much easier on learners if they get stuck on a complicated problem, and it also gives them access to more in-depth practical feedback so they can improve on future projects.
Coursera’s teaching setup is more structured, like what you’d experience at a real college or university. Though you can go at your own pace if you want, syllabi and schedules provide a framework for when you should be doing things (or have them done). Compared to Udacity, machine learning courses on Coursera tend to be based more heavily on theory and math. So it will really help you if you have a background in statistics and/or computer programming. Another downside is that there’s less emphasis on one-to-one help on Coursera than there is on Udacity. Instead, it’s more of a “help-by-community-discussion” setup.
There are assignments sprinkled throughout courses on Coursera to check your progress. If you’re doing a “Specialization” set of courses, there’s also a “capstone” project at the end for you to put everything you’ve learned together in order to solve a multi-step problem. Coursera has also implemented a new feature called the Coursera Project Network, which allows you to tackle multi-step assignments with assistance from expert educators – much like you would in a Udacity Nanodegree program.
However, as it’s relatively new, the Coursera Project Network isn’t fully integrated with other courses and programs on Coursera right now. So to find a project that matches your skillset, you might have to do some extra searching around. These projects also have some accessibility issues, like only being available in English and on desktop computers. You also have to pay for them separately, though most aren’t that expensive (usually around $10).
When everything’s said and done, what tangible benefit can you get out of a machine learning program on Udacity or Coursera? And more importantly, where can it take you? Let’s have a look.
Udacity isn’t officially a university, so you can’t get any academic credit from it (with one exception: taking certain courses can count towards a Master of Computer Science degree from Georgia Tech). However, there are other possible benefits to taking and finishing a Nanodegree course. Udacity has a career services division that will work with you after you pass a Nanodegree, and not just to spiff up your employability documents (resume, CV, cover letter, portfolio, online profiles, etc.). They may even be able to land you a job with one of Udacity’s corporate partners!
Nanodegrees also often take less time to complete than comparable programs on Coursera, usually lasting only 3 or 4 months if you’re dedicated enough. So if you’re looking to break into the machine learning industry quickly, Udacity might be a better choice.
Coursera supports a wider range of learning outcomes for machine learning than Udacity does. Like on Udacity, you can take some courses for free, but you won’t gain anything other than knowledge (and bragging rights). Or, you can pay for a course – or a group of related courses called a “Specialization” – to get a certificate of achievement if you pass. This doesn’t count as academic credit, but it does look nice in a portfolio or on a profile/resume.
Coursera also has “Professional Certificate” programs, which are fairly similar to Udacity’s Nanodegrees. You do a curated set of lessons and projects, and if you pass, the company presenting the program may decide to hire you. You can even do a genuine online Master’s degree in a field that includes machine learning as a component skill, if you so choose.
On the other hand, Coursera’s outcomes take a fair bit more time than Udacity’s to realize. Professional Certificate programs on Coursera related to machine learning take between 6 and 10 months to complete. And an online Master’s degree can take anywhere from 1 to 4 years. So Coursera may be better if time is more on your side than money is.
We hope you’ve gained some insight from our comparison of Udacity and Coursera in teaching machine learning. Maybe it has even helped you make a decision about which platform you want to study on. If you’re still undecided, consider reading our full review of Udacity vs. Coursera!