How to Work in AI Without a Degree

Artificial intelligence (AI) is a branch of computer science. Its focus is to create machines capable of making decisions and solving problems like humans. Self-driving cars and virtual assistants are real-world examples of AI.

Are you interested in an artificial intelligence career? There are many ways to begin working in AI and choosing not to earn a degree is one of them. Instead of earning a degree to start an AI career, you can network with AI professionals, learn programming languages, and take online courses.

Below, we'll explore the steps you can take to get an AI job without a degree. We'll also review some great AI career paths for non-degree holders.

Can You Get a Job in Artificial Intelligence Without a College Degree?

Yes, you can start a job in artificial intelligence without a college degree. While a bachelor's degree is commonly required for AI prompt engineer and data engineer careers, you can enter some entry-level and intermediate roles by getting the right background and hands-on experience in the field.

Potential employers want to know they're hiring someone with top-notch AI skills. Problem-solving and analytical skills are also vital to success in an AI role. Showing you qualify for your desired position can help you snag your dream job.

Can You Become an AI Researcher Without a Degree?

Some employers prefer AI researchers with a bachelor's degree. However, you can qualify for this role without it if you understand the following:

• programming languages like Python and R
• data structures and basic algorithms
• data management
• machine learning
• AI development
• data science

Your AI research role might require you to develop AI technologies. You might also be asked to implement model optimization techniques. If you want to secure this or other AI roles, keep reading to learn the steps you can take to get an AI job without a degree.

Steps To Get an AI Job Without a Degree

Entering the field of AI requires you to understand programming languages and other fundamentals. Securing one or more certifications is also important for many AI roles. Let's look at these and other steps you can take to qualify for an AI job.

1. Learn Programming Languages

A programming language involves a set of instructions a computer programmer has created. The instructions produce responses and outputs that computers interpret and carry out. The following programming languages align well with AI projects.

Python

Python is considered a general-purpose programming language. It can be tied to data science and machine learning, making it a popular choice for AI. Python has pre-designed libraries that enhance AI development. TensorFlow and Keras facilitate deep learning. Scikit-learn supports regression and classification.

Java

Java is an object-oriented programming language. It is known for having a faster runtime and quicker execution than Python. This programming language is often used in mobile app development and machine learning projects. You can also use it for deep learning, natural language processing (NLP), and data analytics.

C++

C++ is also an object-oriented programming language. Its fast processing speed helps complex machine-learning models run smoothly. Deep learning and machine learning libraries are often written in C++. SHARK and MLPACK or common C++ libraries.

2. Pursue Certifications

Tech companies want workers with a wealth of AI knowledge. Certificates and certifications help those seeking AI jobs prove their knowledge of the field. Here are some to consider.

Google AI Certification

Google offers the Professional Machine Learning Engineer certification. It tests AI experts' ability to do the following.

• Architect low-code machine learning solutions.
• Scale prototypes into machine learning models.
• Automate machine learning pipelines.
• Monitor machine learning solutions.

This certification also ensures you can work well with team members. The exam doesn't assess coding but does test knowledge of Cloud SQL and Python.

IBM AI Engineering Professional Certificate

The IBM AI Engineering Professional Certificate prepares individuals for AI engineer careers. You'll learn how to do the following.

• Describe ML algorithms like clustering and regression.
• Explain deep learning, neural networks, and machine learning.
• Deploy ML algorithms.
• Use PyTorch, Keras, and TensorFlow to build deep learning models and neural networks.

This six-course series ensures AI engineers know how to use machine learning and deep learning in business. Students can enroll for free through the Coursera learning platform.

Microsoft Certified: Azure AI Engineer Associate

Microsoft Certified: Azure AI Engineer Associate ensures you're ready to perform as a Microsoft Azure AI engineer. This certification exam tests your ability to do the following.

• Implement generative AI solutions.
• Manage an Azure AI solution.
• Implement NLP and content moderation solutions.

Microsoft offers courses to help prepare for the certification exam. Once earned, you should renew this certification every 12 months.

3. Take Online Courses

Online courses can grow your AI knowledge, give you technical skills, and teach you to use important AI tools. You can take them from home while gaining the practical experience you need to enter the job market. Courses might cover:

• artificial intelligence basics
• data science
• machine learning
• deep learning
• neural networks
• programming languages

Some courses are offered through online learning platforms like Coursera and edX. You could also enroll in a bootcamp that accelerates the learning process and prepares you for AI careers in less time.

4. Network With AI Professionals

AI professionals have years of experience and plenty of knowledge to share with newcomers to the field. Networking with experts in the AI community gives you insight into courses you can take, certifications to consider, and the best ways to conduct your job search. If you don't know AI professionals, attend IT conferences and workshops. Also, visit AI forums and social media platforms like LinkedIn.

5. Build a Strong Portfolio

Before you start your job search, you'll want to create a strong portfolio showcasing your top AI skills. Here's what you can include:

• programming languages you've mastered
• AI projects you've completed
• resume with work experience in AI
• testimonials and recommendations

The more you include about your AI background the better. However, be sure that your specialized AI skills stand out the most.

Alternative AI Career Paths for Non-Degree Holders

AI is one of many computer and information technology occupations. According to the U.S. Bureau of Labor Statistics (BLS), these occupations expect much faster than average employment growth (more than 377,000 openings annually) from 2022 to 2032. Roles like those listed below could be secured without a degree.

Machine Learning Engineer

Machine learning engineers create the software that supports ML applications. This role relies on knowledge of data science and programming to complete tasks like the following:

• loading data into ML models developed by data scientists
• analyzing how data going into ML models might be applied to different use cases
• coding algorithms that power machine learning applications

Companies sometimes prefer ML engineers with a bachelor's degree in computer science, IT, or a related field. However, some hire workers with five or more years of experience in machine learning and analytics.

Software Engineer

Software engineers develop software products and customized systems. Some duties include:

• conducting system analyses to improve systemic operations
• investigating network and product bugs
• securing and licensing software

Software engineers must have strong software development, coding, and engineering knowledge. You can gain experience for this role through coding bootcamps or self-directed learning.

Data Scientist

A data scientist analyzes data using specific analytical tools and techniques. Some of their duties include:

• collecting and categorizing data
• deciding which data are useful for projects
• creating, testing, and updating models and algorithms

Some employers might seek a data scientist with a bachelor's or master's degree. However, others look for candidates with ample experience in data analytics.