Fresh Out Of College & No Experience? Here’s How To Get An AI Job

AI is currently booming and with the kind of advancements that are happening, there is no stopping. Every industry, from manufacturing, retail, pharmaceuticals, to healthcare and finance, uses AI and machine learning (a subset of AI) tools to automate mundane tasks, sift through several GBs of data to make an accurate business decision, and improve customer service amongst many other tasks. So while this is all true and evident, everyone wants to hop on the train that is artificial intelligence. So how can a software graduate land a job

The Prerequisites

Artificial Intelligence is a unique field. As a young graduate, you might not have any solid information about the field and no relevant experience. A few modules in colleges will not be much of help, and employers have a hard time looking for candidates with relevant experience.

There is nothing daunting about this, students. This is a learning opportunity.

Currently, there is a huge shortage of ML (machine learning) developers. If you show them you have the experience they are looking for, you will be selected. But if you're wondering why there is a shortage in the first place, you are an analytical thinker. AI is a tough area to become an expert in as advancements happen at a rapid pace and it's hard to keep up.

In order to build your brand in this field, you will need:

Experience

Understanding Of Theory

Specialization

College

The Break Down

Experience

Experience doesn't necessarily mean a history of working for an ML company. Personal projects, hackathons, coding challenges, open-source projects is an experience that also matters.

Personal projects are crucial. Not having personal projects in the CV is a quick way for recruiters to eliminate people. These projects don't have to be big and flashy. No one expects a student to come up with a revolutionary concept. Your personal projects should show your understanding of the topic and your capacity to work and research independently with good coding standards. A bonus is to show projects that didn't take longer than a month to complete, have a clean and modular code, have all the references of the technology you used, and unit tests for key parts of the codebase.

Hackathons and coding projects indicate that you know the practical application of what you have learned. Isn't that the whole essence of machine learning?

Open source projects are as close to a real project experience as a student can get. These projects will give you a sneak peek into the production-level code and teach you valuable skills like debugging, versioning control, teamwork, and lots of machine learning.

Understanding The Theory

It's important to understand what you are building. There are many resources to go through the important theories of ML and deep learning. Getting familiar and thoroughly understanding concepts like regression models, support vector machines, probability, and statistics will always be valuable no matter what kind of AI you are building. Check out resources like Stanford Machine Learning, DeepLearning.ai, and Grokking Deep Learning.

Specialization

ML is a massive field and employers know that it's impossible to know everything. This is why specialization is necessary, to have expertise in one area. This shows that you have a strong understanding of machine learning and deep learning and you have specialized in one field with valuable knowledge. Specializing in areas like computer vision, recurrent networks, reinforcement learning, natural language processing, meta-learning, one-shot learning, neural network visualization, and debugging will take you far in this field and distinguish you from the pool of candidates.

College

While many tech giants like Apple and Google have started to waive their requirement of bachelor's degrees, doing well in college is a bonus. Innovative companies understand that the qualities they are looking for, like passion, being self-driven, first to take initiative, etc. don't necessarily require a college degree. So, if you could not attend college for some reason, accomplishing every other thing in this article will help you out. But if you are in college, focus on getting good grades. This shows hard work and determination.

This is the best time to enter the AI industry. As discussed AI has made its place in every industry and it did it while being in the initial phase, the learning phase. As the AI field is in a constant state of advancement, you can get to be a part of many revolutionary innovations that can change the way we live and work.