Professionals who shared insights on Quora emphasised that AI may look complex, but beginners can make steady progress by focusing on fundamentals and solving real problems.

Start With the Basics — Then Build

Tech enthusiast Tony Lange cautioned aspiring learners against chasing shortcuts.

“There are no shortcuts,” he noted, advising those with programming and mathematics backgrounds to seek real-world exposure by collaborating with experienced teams.

According to him, one of the biggest mistakes beginners make is consuming endless theory without applying it. Practical experience — even on small projects — is what transforms interest into competence. Learners working independently, he said, should connect with communities where they can ask questions and compare approaches.

However, Lange stressed that foundational skills in coding and mathematics remain important for technical AI roles, warning that a lack of programming experience can create significant barriers.

You Don’t Need to Spend a Fortune

For those concerned about cost, other experts say financial barriers should not be an excuse.

Asher Alex pointed out that many introductory AI courses, tools and communities are free. Open-source platforms now allow beginners to experiment with machine learning and data science tools without paying for expensive software.

“The key is to build small projects,” Alex said. “Don’t wait until you feel fully ready.”

Joining online forums, social media groups and local tech meet-ups can accelerate learning through shared knowledge and peer support.

One Step at a Time

Corporate trainer John Benfield described AI learning as a gradual journey.

“Like eating an elephant, just do it one bite at a time,” he advised.

Benfield encouraged beginners to first become users of AI systems before attempting to build them. Understanding how tools work at a high level helps learners identify which areas — software engineering, mathematics, hardware, neuroscience or data engineering — best match their interests.

Artificial intelligence spans multiple disciplines, and mastery does not require expertise in all of them. Curiosity and experimentation, he said, are more important at the early stage than perfection.

Practical Exposure Over Theory

VMware executive VMware’s David Care shared that the effectiveness of learning depends more on approach than on the specific course chosen.

He advised beginners to prioritise courses that combine theory with hands-on implementation. Before using advanced AI frameworks, he practised building neural networks from scratch to understand underlying principles.

Care also highlighted a common misconception: most real-world AI work is not glamorous model building.

“Eighty per cent of real AI work is data cleaning and infrastructure,” he said, explaining that debugging models, managing data pipelines and optimising performance are where much of the learning happens.

Understanding Modern AI Systems

Computer scientist Miguel Paraz offered perspective on modern large language models (LLMs). While programming skills are helpful for building applications on top of such systems, he explained that understanding their internal workings can be challenging because they operate as highly complex, data-trained “black boxes.”

“To learn LLM AI, just talk to them,” he suggested, encouraging experimentation and exploration.

The Bottom Line

Experts agree on a few core principles:

Build strong foundations in programming and mathematics.

Work on real projects as early as possible.

Use free courses and open-source tools.

Join communities and ask questions.

Focus on data handling and practical execution.

Stay curious and keep learning.

AI is evolving rapidly, but the pathway into it remains grounded in patience, experimentation and consistent practice. Those willing to learn by doing — rather than waiting for perfect conditions — are far more likely to succeed.