How to break into machine learning and AI in 2024

With the advanced computing power of modern-day machine learning (ML) and artificial intelligence (AI) technologies, the ability to automate and streamline processes has turned the tech industry into a competitive and fast-paced landscape. Businesses are continuously seeking to implement data-driven strategies and implement efficiency in their operations in order to gain an edge and keep up with the ever-evolving field; in turn, this need for constant innovation has created an unprecedented demand for ML and AI professionals.

As the surge in ML/AI role postings and opportunities continues to grow, it’s the perfect time to consider a career in data science: whether you’re a complete beginner to the field or you’ve already got a little bit of coding knowledge up your sleeve, this article will walk you through some actionable steps you can take today to launch your career in tech. This includes steps like taking a Bootcamp to fast-track learning the foundational skills you need to succeed in the fast-paced world of ML/AI.

Understanding machine learning and AI

Before we can explore all the different ML/AI career pathways that exist and how you can set out on your own journey, we first have to ask the big question: what even is machine learning and artificial intelligence anyway? Most people throw these terms around without knowing their definitions, and some even use them interchangeably!

To put it simply, machine learning is a means to an end: it is a way to achieve artificial intelligence (AI) and is, in turn, considered a branch or subset of AI. Machine learning can be defined as the process of developing algorithms and statistical models that are then used to analyze data and recognize patterns. Artificial intelligence is essentially the byproduct of machine learning and is more concerned with the creation of computer systems and technologies that can then be programmed with machine learning algorithms.

Both ML and AI are vital to today’s tech landscape for a variety of reasons: these technologies ultimately help us make data-driven decisions, streamline and/or automate manual processes, and discover new insights and patterns. To learn more about the difference between ‘machine learning’ and ‘AI’, check out our blog post here.

(source: https://usmsystems.com/difference-between-ai-and-machine-learning/)

Starting with education

If you’re still a bit confused after reading the paragraph above, don’t worry: while there is so much more to learn about ML and AI, the good thing is that there are so many different (and often free!) resources, platforms, and online courses out there to help you better understand these fields. However, just like building a house, becoming an expert in ML/AI starts with creating a solid foundation to build upon. Learning data science fundamentals is the key to growing a career in this field, as having basic and transferable skills will open doors to various different career paths in the tech industry.

There are many ways to go about education in machine learning and AI: the most traditional route, of course, is to turn to academia and pursue an undergraduate degree in subjects like math, computer science, computer programming, statistics, or physics. Depending on the specific role you are interested in, it also may be worthwhile to complete further education and earn a postgraduate degree such as a master’s or a PhD. The major benefit of pursuing a second (or third!) degree is that programs tend to get much more concentrated at the postgraduate level: for example, there are plenty of master’s and PhD programs that are specifically focused on artificial intelligence and machine learning, as opposed to the more general nature of undergrad.

However, since traditional higher education can often be rigid and time-consuming (undergraduate degrees are typically 4-year programs), there are also more flexible ways to learn about data science, including online courses and bootcamps. For example, Lighthouse Labs’ Data Science Bootcamp is offered in the form of a full-time 12-week Bootcamp or as a part-time 30-week Bootcamp to best suit your unique schedule and learning preferences.

Whichever education path you ultimately choose to take, some key subjects to study include:

• Data wrangling

• Data visualization

• Coding/programming (e.g. Python, SQL)

• Probability & statistics

• Calculus & algebra

• Machine learning

• LLMs for NLP

Building practical experience

Although formal education is a great way to kick-off your theoretical understanding of ML and AI, there is also something to be said about gaining hands-on, real-world experience. This is especially important to employers, as tangible examples of your work can often showcase and speak to your abilities in a much more effective way. This is why ML/AI professionals should develop a personal portfolio of projects to submit alongside their resume in job applications.

A portfolio should showcase past learning experiences and projects that demonstrate your ability to apply your knowledge in real-world scenarios. Machine learning projects can be assignments you worked on in school or even algorithms that you developed on your own time. For each one of your projects, make sure to include an explanation of the goal you were trying to achieve, the tools, programs, and/or methods you used, and the final results and learnings from the project.

When beginning your portfolio development, consider how you can make yours stand out: you may want to add multimedia or visual elements that elevate your page, like recording yourself talking through a previous project. If you have a GitHub profile, make sure you link to that as well so employers can access your projects.

Transferable skills to machine learning and AI

Since data science is such a large and broad field, it is crucial to pick up skills that can be applied in a variety of contexts. This includes having mastery of both technical knowledge as well as vital interpersonal or ‘soft’ skills. Having the flexibility to apply your skills in different roles can be a valuable asset, as job descriptions and titles are often not as ‘cookie-cutter’ as they are in theory and may vary from company to company.

Some transferable skills for tech include:

• Communication: ML/AI professionals must be able to translate technical language and jargon into simple terms so as to communicate the value of their work in the context of business goals. If asked about your communication skills in an interview, provide an example of a time when you successfully conveyed a technical message to an executive or leader who was not a subject expert.

• Problem-solving: Errors in data can show up frequently in this field, so it is important to be solution-oriented and resilient when faced with a challenge. When asked about problem-solving in an interview setting, think about a time when you developed a creative solution to a problem. You may also want to detail how you came up with the solution to demonstrate your critical thinking abilities.

• Teamwork: Although a lot of machine learning and AI professionals work predominantly independently, it is also important to know how to collaborate with others and work with differing personalities. When preparing for interviews, write down some examples of when you successfully completed a task or project in a team setting, and describe any actions you took to create a plan and/or equally divide the work.

• Adaptability: Since the tech world is constantly changing and evolving, it's important for ML/AI professionals to be flexible and comfortable in uncertain situations. For interview preparation, think of a time when you had to pivot your strategy on a project or adapt to learning a new technology or platform.

To read more about in-demand technical skills to learn, check out our blog post here.

Upskilling and continuous learning

Since the technology field is so fast-paced and constantly evolving, it’s equally as important to stay up-to-date with emerging AI technology trends and new certifications or skills. For this reason, it’s critical that prospective data science professionals consider themselves to be lifelong learners with a relentless curiosity and a keen interest in continuous learning in ML/AI.

So, where do you go when looking to advance your knowledge and upskill? Well, as mentioned in the education heading above, there are lots of free resources to check out online, including platforms like Kaggle and DataCamp. Lighthouse Labs also offers free courses, like our Programming Essentials with Python course, where you can self-study at your own pace. However, one of the most important things to keep in mind, regardless of what learning avenue you end up taking, is to make sure that you’re consuming relevant and up-to-date information. Keep an eye out for resources that have a ‘published’ or ‘last updated’ date: this will give you a good indication of how recent the information is.

At Lighthouse Labs, we know firsthand the importance of keeping up with new developments, which is why we recently upgraded the curriculum of our Data Science Bootcamp. Part of the update includes a new module that focuses on using large language models (LLM) and natural language processing (NLP) for solving business problems in the context of data science, so rest assured you’ll be receiving up-to-date information.

Applying to jobs

Now that you’re armed with the education, experience, and skills that you need, you’re ready to start job hunting in ML/AI! Although kicking off the job search may seem intimidating or daunting at first, it’s truly the first step that is the most difficult. With that said, the best piece of advice is to do that: just start! Log into LinkedIn or Indeed and start sifting through recent postings to familiarize yourself with different kinds of job titles that are out there, the necessary qualifications, and which companies are actively hiring.

As a quick reference, below are some ML/AI common job titles to look out for in your job search, along with their requirements:

• Machine learning engineer: Design and test machine learning systems and develop AI algorithms. Experience with programming languages like Python is required. Read even more about what machine learning engineers do by checking out our blog post here.

• Data engineer: Analyze, evaluate, and maintain data. Proficiency in coding languages and an understanding of data visualization and data pipelines are required.

• AI research analyst: Collect data to make hypotheses and conduct research to discover new knowledge in the AI field. Experience working with AI frameworks like TensorFlow or PyTorch will be an asset.

To learn more about artificial intelligence roles in particular, read our blog post here.

Another key strategy when it comes to finding ML/AI roles is to reach out and connect with people in the field. Networking in tech is not only a great way to learn about the realities of working in the industry, but it also helps you meet new people and build genuine connections that can come in handy when submitting applications. If you apply to a company where a connection works, for example, they can often submit a referral on your behalf or provide you with the contact information of a recruiter or hiring manager. This is critical in making sure your application stands out: given the competitive job market nowadays, it is (unfortunately) no longer enough to simply send your application into the ether, so having a connection may at least help get your foot in the door.

A great place where you can begin to network and build connections is in your local school or community groups. At Lighthouse Labs, for example, we have an Alumni Discord channel where our engaged and active network of graduates can keep in touch. We also regularly host Demo Days, guest speakers and alumni events in order to enable grads to connect with each other.

Success stories

Speaking of alumni, we have a number of Lighthouse Labs graduates who have successfully transitioned into ML/AI roles following their education. A recent grad from our Data Science Bootcamp, Samuel, says that, with the help of our Career Services team, he was able to land a role right after completing the Bootcamp. “My Career Advisor at Lighthouse Labs was a huge source of support and encouragement, and gave me several job search tips and strategies,” says Samuel. “The Junior ML Developer role I landed was through her recommendation to one of the Lighthouse Labs partner organizations.”

It’s also interesting that many of our alumni who have gone on to work in data science actually transitioned from other fields and industries. For example, another recent Data Science grad, Jack, was initially studying environmental science before he enrolled in the Lighthouse Labs Bootcamp. After completing the Bootcamp, Jack received job offers from three different employers and ultimately took a job as a data scientist in the manufacturing, oil and gas industry. So, if you’re like Jack and are pivoting into ML/AI from a completely different career path, try not to get discouraged: Jack’s story proves that with some further education and hard work, anything is possible.

Although taking the first step can often be intimidating and scary, remember that there are a variety of resources that exist, whether at Lighthouse Labs or elsewhere, that are there to support you along your journey to ML/AI. Also, keep in mind that even the most skilled and talented data science experts out there were once beginners too! If you take away anything from this article, let it be the motivation to take the leap and start your career in machine learning and artificial intelligence.

Some actionable steps you can take today include:

• Researching jobs you are interested in and what qualifications they require (check out LinkedIn, Indeed or other job boards)

• Signing up for/enrolling in data science courses or programs

• Connecting with ML/AI professionals and arranging coffee chats

Ready to dive into the world of AI and machine learning? Apply to our new Data Science Bootcamp and start building the foundation for your new career today. Download our Data Science Bootcamp Curriculum Package now and explore the roadmap to joining the machine learning and AI revolution.