Over the past decade, bootcamps 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. After all, bootcamps preserve much of the flexibility and affordability of massive open online courses (MOOCs) while also presenting learners with more opportunities to get facetime with a mentor and to network with a learning cohort.
If you research the most popular bootcamps out there, you’re likely to see a common theme: money. Bootcamps are valuable precisely because the training offered is intended to help students gain the skills and experience needed to land a lucrative position in today’s hottest fields: coding, data science, UX design — the list goes on.
It’s no surprise, then, that recently bootcamps have started popping up promising to provide training in machine learning. Machine learning engineers topped Indeed’s Best Jobs of 2019 list with a reported average base salary of $146,085, compensation that corresponds to a heightened demand across almost every industry for professionals with machine learning skills.
Have you been considering jumping into machine learning, but don’t know where to start? Does the prospect of a high salary and a constantly expanding and accelerating field seem attractive, but you’re not sure if you want to sink time and money into a specialized machine learning bachelor’s or master’s degree in machine learning? Perhaps you already have experience in programming or data analysis but want to upskill to work in artificial intelligence or machine learning.
In this article, we’ll give you a breakdown of what you’ll need to know before you enter a machine learning bootcamp, what you can expect to get out of one, what you should look for when deciding on a program, and where you might end up after you graduate.
Deciding between machine learning and data science bootcamps
Before we dive into the particulars of machine learning bootcamps, it’s important to address what you likely already know: there aren’t many machine learning bootcamps out there! When a school or educational organization advertises machine learning in bootcamp-form, it’s more often than not a data science bootcamp — so what’s the difference?
According to IBM, data science is “a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. [It] encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.” A data scientist follows data through the data pipeline:
Identifying research interests and questions
Engaging in data preparation to turn unstructured data (or “raw data”) into useable data
Analyzing that data, often with the help of machine learning algorithms and models
Using data visualization tools to persuasively communicate findings
Machine learning, on the other hand, is a subset of artificial intelligence that focuses on the development of mathematical algorithms that allow computers to progressively improve their capabilities — “learning” as they go. As Stuart Russell and Peter Norvig put it in Artificial Intelligence: A Modern Approach, the leading AI textbook, in machine learning “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.”
So while a data scientist focuses on extracting business intelligence from big data sets, frequently employing machine learning algorithms and models to do so, machine learning itself has important applications that extend beyond mere business analytics, including medical diagnosis, image recognition, and product recommendation.
What does all this mean when looking at bootcamps? It means that if you are looking for a career where you have to develop a broader skill-set, with projects always driven by business goals, opt for a data science bootcamp — you can find some great recommendations over at Data Science Programs. If instead, you are eager to dive deep into machine learning and spend your life fiddling with deep learning neural networks and other machine learning models, then programs focusing on machine learning are right for you. Of course, nothing is stopping you from leveraging these skills — which all have applications in data science — to become a data scientist down the road.
Who is a machine learning bootcamp right for?
So we’ve covered if a machine learning bootcamp is right for you — but are you right for a machine learning bootcamp? Due to their highly technical nature, shortened instruction schedule, and frequent reliance on self-study, many machine learning bootcamps set formal or informal prerequisites for applicants to ensure that those they admit will succeed.
While each program is different, most are looking for applicants who already have significant experience in either computer programming or mathematics — and preferably both. While formal work experience and a bachelor’s degree in computer science or a data-heavy STEM field will be the most obvious indications of these skill sets, many machine learning bootcamps are also open to applicants who can demonstrate that they’ve developed these skills through self-study and independent projects.
If in doubt about your eligibility, make sure to check the specific requirements for each program before applying.
What can you expect to get out of a machine learning bootcamp?
Noting again that all bootcamps are built differently, a student taking a machine learning bootcamp can generally expect to focus on 1) learning basic theory behind artificial intelligence and machine learning, 2) building key skills and familiarity with the software that a machine learning engineer works with every day, and 3) completing a capstone project that will show prospective employers that the student can apply everything they’ve learned. We’ll dive into each of these in greater detail now.
Artificial Intelligence and Machine Learning Theory
Before learning to do it, you need to learn what it’s all about. Most machine learning bootcamps will begin with an introduction to artificial intelligence and machine learning, sometimes focusing on their business applications rather than more esoteric philosophical distinctions. Because of the shorter format, you can expect these introductions to be far terser than would be in a degree program: they’ll give you just enough context to get you started building skills like writing machine learning algorithms.
Machine Learning Skills & Software
The core of a machine learning bootcamp’s curriculum comes in the form of practical training that furnishes learners with an arsenal of machine learning skills. These include the ability to write machine learning algorithms (the fundamental bits of code that allow computers to learn) and develop a machine learning model (several algorithms put together to complete a more complex task). To do this, students will usually also receive some training in statistics, if they haven’t had any before, and in programming languages like Python or R.
Machine learning bootcamps, especially if they extend to more advanced concepts, will also cover the key software a future machine learning engineer needs to be comfortable with. For example, Caltech’s Artificial Intelligence and Machine Learning Bootcamp, powered by Simplilearn, offers a course that teaches TensorFlow and Keras, two machine learning platforms, to teach deep learning. When building on this learning and introducing computer vision, the Caltech bootcamp also allows students to become comfortable with PyTorch, an open-source machine learning framework developed by Meta.
In Caltech’s program, you also have to take electives that will teach you the basics of advanced topics like natural language processing or NLP (machine learning systems that learn to recognize and produce language) and reinforcement learning (a machine learning technique that uses numerical rewards to incentivize learning.)
As we will discuss in more detail later on, a machine learning bootcamp must offer practical software training. Many jobs, even entry-level ones, will require that applicants already be familiar with many popular platforms and software libraries.
Machine Learning Capstone Project
Many applicants will be able to say they are familiar with different software, so what will set you apart from the rest of the applicant pool for a job will be how well you can use them. For this reason, many bootcamps will end with the development of a capstone project that students can add to their portfolios.
At the University of California San Diego Extended Studies' Machine Learning Bootcamp, for example, the capstone project is designed to give learners the experience of an end-to-end machine learning project in an industry setting. Learners are free to choose a project according to their interests, and, with the guidance of a mentor, work through ideation, data collection, prototyping, and deployment phases. This process will result in a host of deliverables that can begin populating the portfolio that companies will review during the application process.
Where can an ML bootcamp get you?
So you’ve completed a machine learning bootcamp and have an awesome portfolio to show for it — what next? The best bootcamps will offer extensive career guidance that will help you identify positions based on your qualifications and interests and submit an application that will set you apart from the crowd. UCSD’s ML bootcamp, for example, offers a dedicated student advisor and 1:1 career coaching that will guide you through job searching, informational interviewing, resume and cover letter writing, and interviewing.
Unless you have had significant success in another field, it’s unlikely that a machine learning bootcamp will let you jump too much higher than an entry-level machine learning engineer position out of the gate. Especially in a field as technical as machine learning, real-world experience is crucial and can’t be faked, even by the best bootcamps.
And there’s a reason for that: an entry-level machine learning engineer position already comes with a hefty salary and ample opportunity to grow. There are, of course, also other paths you can take. UCSD advertises their machine learning bootcamp as good preparation for jobs with titles including data scientist, NLP scientist, business intelligence developer, and research scientist.
What should you look for in a machine learning bootcamp?
You’re ready to take the plunge and apply to a machine learning bootcamp, but how do you know which one to pick? As you look, use the following to guide your decision:
The first, and most important, criterion that should guide your decision is student outcome. If a program can’t show proof of concept — that its curriculum can help a large percentage of its students find a stellar job — then it’s likely not worth your time and money. When possible, use a trusted third-party source for this information to ensure you’re getting the straight dope.
One of the reasons people seek out bootcamps in the first place is their promise of a lower cost of entry to better career prospects — but you’ll want to make sure this is the case. Take a program’s total cost of attendance (including any lost salary if you are taking time off work) into consideration along with student-outcome data to be confident of a good return on investment before you pay your first tuition bill. And always exhaust financial aid opportunities like private scholarships and grants and federal grants, scholarships, and loans before resorting to private loans if you don’t have the means to pay upfront.
There are a lot of people moving around in ed-tech right now, and they can’t all be trusted. Before you pay any tuition, you want to make sure that the course curriculum has been developed by an accredited educational institution committed to delivering you the right information in the right way.
Most bootcamps will offer written material and pre-recorded videos for most of the instruction, and you’ll want to make sure these are complemented by 1:1 mentorship. Mentor sessions are a space to ask any questions that pop up as you tackle the material and gain insight into how the concepts, techniques, and software you are learning translate in the real world.
Whether as a part of the mentorship component or separate from it, it’s also crucial that a machine learning bootcamp offers your career services to put what you’ve learned to work. Try to find a program that will offer live support for resume building, networking help, and job searching. If a machine learning bootcamp has existing relationships with employers, all the better!
Check out our machine learning bootcamp recommendations!
It can be difficult to keep all the different factors in mind when researching machine learning bootcamps. To help, we’ve gone ahead and done this work ourselves. On our machine learning bootcamp recommendations page, you can find programs we’ve vetted according to the criteria listed above — so you can apply with “peace of mind.”
If after reading this you’ve decided you’d rather shoot for a machine learning bachelor’s or master’s degree, we can help you there, too! Head over to our degree program primer to get started.