Experts say learning artificial intelligence requires curiosity, a solid foundation, and practice with real projects, rather than relying solely on theory and expensive courses. Justice Okamba I will write
Everyone is talking about artificial intelligence and it has become a household name. It continues to reshape industries, from finance to healthcare to education to media. Experts say the path into the field is easier than many think, but requires patience, hands-on effort, and a willingness to continually learn.
Technical experts and trainers writing on the subject on the social learning platform Quora emphasized that while AI may seem complex, beginners can break into the field by focusing on the basics, tackling real-world problems, and taking advantage of widely available free resources.
Technology enthusiast Tony Lange said those willing to learn first need to understand that there are no shortcuts to building artificial intelligence capabilities.
“There are no shortcuts,” Lange wrote. “If you have a background in programming and mathematics, start working with a team in that field. I doubt they will have the patience to coach you thoroughly, but if you are given the opportunity to work with them, you will learn by doing.”
According to him, one of the biggest mistakes beginners make is spending too much time on theory instead of trying to solve real problems. He explained that practical experience is what separates casual interest from real skill.
“If you don’t have access to a team and you’re on your own, you need to find someone to communicate with and ask questions,” he said. “Then you need to start working on real problems. That’s the trick to starting working on real problems.”
Lange said that by working on real projects, learners can compare their progress with other projects, understand how solutions are developed and identify gaps in knowledge.
However, he cautioned that those without programming experience may struggle with technical AI roles.
“If you don’t have programming experience, you’re in trouble,” he added, reiterating that basic coding and math skills remain important for many AI career paths.
While Lange emphasized the technical fundamentals, other experts stressed that financial barriers should not deter beginners.
Asher Alex said training AI doesn’t have to be expensive.
“You don’t have to spend money,” Alex said. “Start with free courses, join the AI community, and build small projects using free AI tools. That’s the fastest way.”
Alex explained that the online platform currently offers free introductory courses covering machine learning, neural networks, and data science. Additionally, open source AI tools allow beginners to experiment without paying for expensive software.
The advocate added that by participating in communities such as online forums, social media groups, and local tech meetups, learners can ask questions, share ideas, and learn from more experienced practitioners.
“The key is to build small projects,” he said. “Don’t wait until you feel completely ready. Start small and gradually improve.”
Corporate trainer John Benfield believes the learning process in AI is gradual and exploratory, and encourages beginners not to be overwhelmed by the breadth of the field.
“Just like eating an elephant, take it one bite at a time,” Benfield advised. “Start with an introductory course to learn how AI is used and the tools, techniques, and approaches available.”
He encouraged learners to first become users of AI systems before attempting to build them.
“Become a user of AI and find out which areas interest you most,” he said. “Be curious and explore.”
Benfield explained that artificial intelligence spans multiple fields, including programming, statistics, electronics, neuroscience, and data engineering. Therefore, learners do not need to master all areas before starting.
“A lot of people say, ‘Learn programming,’ ‘Learn statistics,’ ‘Learn electronics,’ but AI spans many disciplines, and you don’t need to be proficient in all of them,” he says. “It starts with understanding the functionality and how the different disciplines work together at a high level.”
He added that some learners may have a strong interest in mathematics, while others may prefer hardware implementation, software engineering, or even biological intelligence.
“It’s a vast landscape and you don’t even have a map or destination in mind,” Benfield added. “Expose yourself to as much introductory material as possible and just play around. You may struggle with some concepts, and that’s okay. Once you build your foundational knowledge, your work becomes easier because you have more information to connect new ideas to.”
VMware executive David Care said the quality of learning is less about the specific course chosen and more about how the learner approaches learning.
“Three years ago, when I switched my field from software development to the world of AI, I bounced around between multiple AI courses,” said Kehr. “What I realized is that it’s not the course that matters. It’s how well you approach the course that matters.”
He emphasized that beginners should prioritize courses that focus on practical implementation rather than just theory.
“For beginners, theory is necessary, but project development in a company is ultimately purely practical,” he said.
Before using common AI frameworks, Care says he practiced building neural networks from scratch using basic tools to understand the basics.
“AI is more than just algorithms,” he said. “It’s about data pipelines, computational constraints, and model debugging.”
He added that many beginners are surprised to learn that most AI work involves preparing and cleaning data rather than developing complex models.
“Eighty percent of the actual AI work is data cleaning and infrastructure, not the deep learning concepts you often see in courses,” Care said.
According to him, the best learning occurs when learners engage with real-world problems such as overfitting, underfitting, and optimizing performance.
“That’s where the real learning happens,” he said. “Courses build the foundation and provide structured knowledge, but ultimately you need to focus on your own development and portfolio.”
Computer scientist Miguel Paraz offered a different perspective, especially regarding modern large-scale language models.
“Learning to program is important, but when it comes to today’s large language models, programming knowledge doesn’t necessarily help you understand how they work under the hood,” Paras said.
He explained that these systems primarily function as complex models trained on large datasets and are often described as “black boxes.”
“To learn LLM AI, just talk to them,” he said. “Programming knowledge will help when you build something on top of it, but it’s a black box right now.”
Paras added that research from major AI companies highlights concerns about surveillance and safety in advanced systems, highlighting how rapidly the field is evolving.
