How to Learn AI

Before diving into how to learn AI, let’s define it.

According to IBM, artificial intelligence is technology that enables computers and digital devices to perform tasks (learn, write, talk, play, analyze, or make recommendations) like humans do. In broader terms, AI refers to the field of computer science that is focused on building these technologies, encompassing the sub-fields of machine learning and deep learning.

Understandably, these two interrelated disciplines are frequently mentioned right alongside artificial intelligence because AI is all about helping machines learn through the use of algorithms that exist to make predictions or classifications based on input, or training data.

The why of how to learn AI can be seen in its astonishing growth. As AI continues to be adopted by a broad spectrum of industries, Forbes reports that its annual growth rate is projected at 37.3% from 2023 to 2030, with the value of the AI market is projected to reach $407 billion by 2027.

This rapid growth can be attributed in part to the recent emergence of generative AI tools like ChatGPT, which represents a giant step forward for automation, and the wide-ranging potential that applications with similar natural language capabilities have for business. Generative AI can be used to write code, design products, create marketing content, streamline operations and provide customer service via the application of chatbots. According to a McKinsey report, AI has the potential to enhance certain sectors, like STEM, business, or creative professions while automation may negatively impact those who work in office support, customer service or food service.  

But the reach and utilization of AI goes far beyond the workplace. Social media platforms are using AI extensively to enhance user experience, fight against hate speech, suggest people to tag in photos, filter spam and boost the results of targeted advertising. And because of its significant potential to enhance various aspects of information security, artificial intelligence integration is one of the 15 cyber security trends in our roundup for 2024. 

As in any educational or career journey, your quest to discover all you can about AI, including how to learn AI programming, will be a step-by-step process:

Artificial intelligence is a complex discipline that lies at the junction of computer technology and data science. To position yourself for success, you should familiarize yourself with several fundamental areas, including computer science basics, probability and statistics, mathematics, programming, data structures and algorithms. Each of these basics comes into play in the machine learning, data analysis and programming aspects of AI.

Regardless of how you acquire your education, you’ll need to learn AI theory. This aspect of AI encompasses problem-solving, reasoning and reasoning methods, data manipulation, natural language understanding, computer vision and automated programming.

Without the ability to analyze data, AI would not exist. Machine learning relies on processing massive data sets and many businesses incorporate data-driven decision making, also known as big data or business intelligence, into their strategies. Data preprocessing involves transforming raw data into understandable formats and ensuring its quality, which depends on traits like accuracy, consistency, completeness, timeliness and trustworthiness.

Everyone learns differently and has diverse goals and commitments, so your path toward AI education will be up to you to determine. You might choose to learn on your own, taking part in bootcamps or programs online to help round out your AI knowledge.

You might also explore academic programs that feature AI courses like the ones offered here at DeVry. Some of our tech-focused programs include courses that were designed to provide students with foundational knowledge and skills in data gathering and analysis, while using artificial intelligence to solve business problems and support decision-making.

Our AI integrated programs include our Undergraduate Certificate in Engineering Technology, our Associate Degree in Engineering Technology and our Bachelor’s Degree in Software Development.

Knowledge of AI theory is just one aspect of learning AI. To gain practical, hands-on experience and develop an understanding of algorithms, try building them from scratch with your own AI projects. A project that combines learning AI with your hobbies might be a fun way to help you get going.

Start out with projects that use simple algorithms, then progress to ones requiring a higher skill level.

Some AI tools you might utilize for your projects are:

What does AI’s rapid projected growth mean for the job market, and where will the bulk of these future opportunities emerge? Let’s look at a couple of occupations where working with AI may play a role, and their projected growth based on job outlook data from the U.S. Bureau of Labor Statistics (BLS):

The fields of data science, machine learning engineering and research may offer opportunities for those looking to pursue an AI-focused career.

With organizations using an increasing amount of data to inform their decision-making, the data scientists interpret and extract meaning from it. They then use the information to find patterns and develop solutions that will meet an organization’s unique needs for their growth and market competitiveness. A data scientist’s everyday duties and responsibilities may include:

Machine learning engineers are the AI designers who have one foot in data science and the other in software engineering. They design, develop and deploy machine learning models and turn data-driven insights into practical and scalable applications.

Some of the key responsibilities of machine learning engineers include data collection and reprocessing, model development, feature engineering, model training and model deployment.

They may also:

Research scientists, when working with AI, are professionals who advance the technology through research and experimentation. Their roles often involve inventing new algorithms or making improvements to existing models to work toward or improve business needs. They need a solid understanding of neural networks and deep learning, data science and AI strategy.

They also need:

After you’ve completed your education, your journey to finding a job in AI can begin.

Looking at what level of education a particular role typically requires is a good thing to keep in mind as you begin your job search. For example, the BLS notes that data scientists typically need at least a bachelor’s degree to enter the occupation, but some employers may require or prefer candidates to have a master’s degree or higher.

A multi-pronged approach, like the one we’ve outlined here, is a good place to begin:

AI jobs are listed on most major employment sites, including LinkedIn, Indeed, Glassdoor and Google Job Search. Some more tech-centered job sites like Dice, AI Jobs Board and TechCareers may feature a more robust selection, as well as have information on hiring events and conferences like the Diversity in Tech Awards event.

Organizations dedicated to AI are great potential resources to help you develop new contacts in the industry or to keep pace with developments in this field.

The Association for the Advancement of Artificial Intelligence, Partnership on AI, Project Voice, the Machine Intelligence Research Institute and the Artificial Intelligence Board of America are all organizations with missions that include promoting professional development, tracking emerging AI trends, providing safety and ethical practice resources and publishing the work of their members.

A strong online portfolio with a variety of AI projects can showcase your proficiency in tools and algorithms, and may help you to stand out to potential employers. Make sure to provide the details of your contribution for each project and how it made an impact. These projects, along with the AI tools you’ve used and skills you’ve learned, should also be included on your resume.

A paid or unpaid internship while you’re still in school, or even after graduation, is a great way to kickstart your career. While internships can’t guarantee placement in full-time positions, they can help you develop your professional skills and give you real-life experience in a work environment.

Networking in person at industry-related events or online is another great way to establish contacts that you might keep for years to come, or even help you find a mentor who could answer questions or provide career advice.

With a variety of AI and analytics courses focused on data gathering, analysis, machine learning and other AI-related topics, DeVry is helping today’s students understand the ways AI can be used to solve business problems and support decision-making.

Explore how we are integrating AI topics and tools to develop essential, foundational skills within a variety of courses and programs. Learn more about our Undergraduate Certificate in Engineering Technology, our Associate Degree in Engineering Technology and our Bachelor’s Degree in Software Development as you consider how to upgrade your career with the types of skills expected in the evolving workplace of the future.