There’s no shortage of resources for healthcare workers who wish they knew AI well enough to talk shop with the technology pros who develop the models. The problem is weeding through the offerings to get to what will really work for you.
Happily, a seasoned technology and business writer has done a good deal of the legwork for all interested parties.
“A successful learning journey in AI involves commitment, curiosity and the right resources,” explains Kathryn Pearl Timonera of eWeek. “You can develop a thorough understanding of AI concepts and applications by reading foundational books, experimenting with AI platforms and participating actively in AI communities.”
Expounding on these and other pointers, Timonera lays out five AI learning pathways that anyone can explore without overspending on anything. Here are excerpts from her listicle.
1. Master the fundamentals of programming.
Beginner-friendly platforms such as Codecademy and FreeCodeCamp offer courses that combine theory with hands-on practice, “allowing you to get practical programming experience and solve real-world problems by building data processing programs, analyzing datasets and experimenting with data visualization,” Timonera reports. More:
‘You can also participate in coding challenges on websites such as LeetCode, HackerRank and CodeSignal as a way to improve your coding skills by working with large datasets and optimizing algorithms for AI.’
2. Learn data structures and algorithms.
“There are many free resources to help you learn and understand data structures and algorithms, which allow effective data processing and problem-solving in AI models,” Timonera writes. “YouTube channels such as FreeCodeCamp and CS50 offer free, extensive tutorials on these topics.” More:
‘In addition, online learning platform Great Learning offers free courses, and AI specialists gather in online communities like Kaggle and GitHub to share knowledge and ask and answer questions.’
3. Study machine learning and deep learning basics.
Machine learning is a subset of AI that allows computers to learn from data without being explicitly programmed, Timonera reminds, while deep learning uses neural networks to mimic how the human brain processes information. FreeCodeCamp offers costless courses in machine and deep learning for intermediate learners as well as beginners, she notes. Meanwhile:
‘Kaggle offers real-world datasets for practicing machine learning in fields such as finance and healthcare, which you can use to develop prediction models for hands-on experience.’
4. Engage with AI communities.
Online communities and forums provide “excellent opportunities for enthusiasts to share knowledge and collaborate on projects,” Timonera points out. “GitHub is a place for developers to collaborate on AI projects and share code repositories.” More:
‘Facebook Groups, Slack Communities and LinkedIn provide professional networks where you can interact with experts, attend webinars and participate in collaborative projects.’
5. Keep current with AI trends and technologies.
AI is constantly changing, with new tools powered by new models and new use cases appearing regularly, Timonera writes. Keeping up to date allows you to better understand the new developments in machine learning, deep learning and AI applications, “which in turn lets you expand your abilities, capitalize on developing technologies and pursue new opportunities in fields where AI is being used.” More:
‘The best way to stay up to date with AI’s latest trends is to join AI-related community groups and subscribe to industry leaders’ newsletters, such as those published by OpenAI, Google, IBM, AWS and Microsoft.’
“Whether you want to master deep learning, explore AI-powered tools or create creative solutions, your journey will be influenced by continuous learning and hands-on experience,” Timonera notes. “Stay open to ideas, explore collaborations and be willing to experiment, as AI’s revolutionary power provides limitless possibilities for growth and innovation.”
There’s more. Read the whole thing.
