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How to Learn AI

By Steve Smith

The information presented here is true and accurate as of the date of publication. DeVry’s programmatic offerings and their accreditations are subject to change. Please refer to the current academic catalog for details.


April 19, 2024

9 min read

Artificial intelligence (AI) is all around us. The relationship between humans and computers envisioned in Isaac Asimov’s I, Robot and other works of fiction has yet to be a reality, but AI-driven voice assistants, chatbots, generative AI programs and search engines have become part of our daily lives. 


If you’re thinking you’d like to prepare for a career working with artificial intelligence, stick around because in this article, we’ll explore how to learn AI, along with some of the various aspects of this rapidly growing family of technology, its commercial and ethical implications for our society and how it fits into the various industrial sectors that underpin our economy. 

What Is 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.

Why Learning AI is Important

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. 

How to Learn AI

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:

Understand the foundations

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.

Learn the theory

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.

Learn data processing

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.

AI courses

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.

AI projects and tools

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:

  • SciKit-Learn: A popular tool used with unsupervised and administered calculations, and is considered to be a good option for beginners.

  • TensorFlow: Used for a variety of machine learning tasks, this tool is good for the training and inference of deep neural networks.

  • PyTorch: Developed by Facebook, this tool is used for natural language processing and computer vision.

AI and Job Growth

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):

  • Data scientists: The BLS projects employment of data scientists to grow 35% from 2022 to 2032, with about 17,700 job openings projected each year, on average, over the decade.1

  • Computer and information research scientists: The BLS projects employment of Computer and information research scientists to grow 23% from 2022 to 2032, and about 3,400 job openings each year, on average, over the decade.2


This growth is projected on a national level. Local growth will vary by location. BLS projections are not specific to DeVry University students or graduates and may include earners at all stages of their careers and not just entry level.


Different Careers in AI

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

Data scientist

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:

  • Compiling data from various sources

  • Cleaning and validating the data to be used for analysis

  • Developing automated tools to collect and process data

  • Analyzing large amounts of information to find patterns and solutions

  • Presenting results and proposing solutions to confront business challenges or opportunities

Machine learning engineer

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:

  • Collaborate closely with data scientists to understand problems, the data involved and the desired solutions.

  • Choose the appropriate machine learning algorithms and techniques, or models for a particular problem.

  • Prepare data for model training, an activity that is also called preprocessing.

  • Develop and fine-tune machine learning models, including deep learning models if required on the project.

  • Integrate machine learning models into production systems, a process that is known as model deployment.

  • Conduct thorough testing and validation of models to confirm they are accurate and reliable.

Research scientist

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:

  • Proficiency in computer programming languages like Python, Scala or Java

  • The ability to conceptualize and validate new AI models

  • Strong writing and presentation capabilities to communicate findings and strategy to stakeholders

Finding a Job in AI

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:

Check the job boards

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.

Align with professional AI organizations

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.

Develop your portfolio and resume

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.

Look for AI internship opportunities

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 can help you build long-term contacts

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.

At DeVry, We’re Preparing Students for the AI-Driven Opportunities of Tomorrow

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.

1Growth projected on a national level. Local growth will vary by location. BLS projections are not specific to DeVry University students or graduates and may include earners at all stages of their career and not just entry level.

2Growth projected on a national level. Local growth will vary by location. BLS projections are not specific to DeVry University students or graduates and may include earners at all stages of their career and not just entry level.

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