Guide to Online Machine Learning Courses

Flexibility

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.

Affordability

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 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.

Accessibility

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.

Efficiency

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.

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. For more context on applied use cases, compare our guide to machine learning applications.

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.

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.

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.

Applied mathematics

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.

Programming

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

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.

Certificate programs

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, students 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.

Cohort-based courses

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.

Curriculum

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.

Modality

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

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.

Live

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.

Reputation

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.

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.