This article was originally published on LinkedIn.
In the fast-paced and burgeoning field of artificial intelligence (AI), there’s a lot of excitement — and with it, unfortunately, some manipulative tactics aiming to exploit the Fear of Missing Out (FOMO) among those new to the field.
Recently, there has been an uptick in YouTube ads promoting low-cost entry courses in AI, which are often just the tip of the iceberg.
Once drawn in, newbies are pressured into investing in much pricier advanced courses, fueled by the fear that they might be left behind if they don’t.
But here’s the truth:
AI Learning Should Be Affordable
Many quality resources and courses are available online for free or at a reasonable cost. Renowned universities and institutions offer Massive Open Online Courses (MOOCs) on platforms like Coursera, edX, and Udacity that are either free or cost significantly less than the price tag attached to those mentioned above ‘exclusive’ courses.
Here are several specific resources for learning AI, ranging from beginner to advanced levels, that are free or cost-effective:
Coursera: Andrew Ng’s Machine Learning course is an excellent starting point. You should also check out his Deep Learning Specialization. Many courses are free to audit, and financial aid is available.
edX: Harvard’s CS50’s Introduction to Artificial Intelligence with Python is a solid entry-level course. For more advanced learners, Columbia University’s MicroMasters Program in Artificial Intelligence covers in-depth topics.
MIT OpenCourseWare: MIT’s Introduction to Computer Science and Programming Using Python and Introduction to Computational Thinking and Data Science are free and excellent foundational courses. They also have an advanced system, Artificial Intelligence.
Udacity: Their Intro to Machine Learning with PyTorch and TensorFlow is excellent for beginners. Udacity’s nanodegree programs, like the Machine Learning Engineer Nanodegree, are paid but quite comprehensive.
Google’s AI Hub: Google’s Machine Learning Crash Course is a free and practical introduction to the field of machine learning.
fast.ai: They offer a highly-regarded and completely free course called Practical Deep Learning for Coders.
Kaggle: Kaggle is a platform for predictive modelling and analytics competitions. They provide a hands-on way to learn data science and machine learning and have free micro-courses covering specific topics.
DataCamp: Offers interactive R, Python, Sheets, SQL, and shell courses on topics in data science, statistics, and machine learning. While not all courses are free, the cost is relatively low.
Stanford Online: Stanford’s Machine Learning course taught by Andrew Ng (also available on Coura) is one of the most highly regarded AI courses online.
Remember, continuous practice is critical to mastering AI. Utilize resources like GitHub for collaborative projects and platforms like Kaggle for competitions to hone your skills.
No Shortcuts to Mastery
AI is a complex field that requires in-depth understanding and hands-on experience. No single course, however costly, can turn you into an AI expert overnight. Mastery comes with time, practice, and continuous learning.
Most estimates suggest that gaining a solid foundational understanding of AI could take anywhere from a few months to a year of full-time study (this could mean completing an online master’s degree or several high-quality online courses).
Becoming proficient, or an ‘expert,’ may require several years of study and practical application, similar to reaching a high level of expertise in other technical fields.
Remember, the goal isn’t just to learn AI but to understand how to solve problems using AI.
This often means focusing less on specific tools or languages, and more on core concepts, problem-solving skills, and creativity. A spirit of curiosity, persistence, and a love of learning are essential to mastery in this field.
FOMO Is Unnecessary
The field of AI is broad, encompassing many specialisations, from machine learning to natural language processing to robotics. There is no ‘one-size-fits-all’ path, and there’s undoubtedly no race. Each learner should find their unique path at their own pace.
It’s essential to evaluate any course before investing critically. Research the course content, check for transparency in pricing, look for unbiased reviews, and compare it with other available resources.
Here are some examples of manipulative tactics that exploit FOMO (Fear of Missing Out) in the context of AI training:
Limited Time Offers: Companies often create a sense of urgency by promoting ‘limited time’ discounts or bonuses, implying that the prospective student will miss a golden opportunity if they wait to sign up immediately.
Exclusive Membership: Promoting the idea that by paying for an expensive course, you are joining an ‘elite’ or ‘exclusive’ group, suggesting that you might miss out on significant opportunities available only to this group.
Successful Testimonials: Showcasing selective success stories, where individuals who took the course have supposedly achieved extraordinary results. This can foster a fear of missing out on similar successes.
Proprietary Learning Methods: Claiming that the course uses exclusive, proprietary learning methods or tools that can’t be found elsewhere suggests that you will miss critical knowledge if you don’t take this course.
Future Fear: Painting a picture of an AI-dominated future where those who don’t understand AI will be left behind, professionally and socially.
Upselling: Initially offering an introductory course at a low cost or free, then continually upselling more expensive courses, suggesting that you will only get the ‘complete’ knowledge by purchasing these.
In learning AI, or any subject, the journey is unique to each person and should not be rushed out of fear of missing out.
AI has a thriving global community. There are numerous forums, groups, and platforms where you can seek guidance, collaborate on projects, and learn from experienced professionals. This collective wisdom can be incredibly helpful and is often free.
Leveraging community support is a powerful way to enhance your AI learning journey. Here are a few ways you can do this:
Online Forums and Discussion Boards: Websites like Stack Overflow and the AI section of Reddit (subreddits like r/MachineLearning, r/learnmachinelearning) can be great places to ask questions and learn from others’ experiences.
Collaborative Platforms: GitHub is not just for storing your projects; it’s also a community where you can contribute to others’ projects, learn from their code, and even collaborate on AI-related projects.
Learning Platforms’ Communities: Many online learning platforms like Coursera, edX, and Udacity have forums for their courses where you can ask questions, share ideas, or clarify doubts.
Local or Virtual Meetups: Websites like Meetup can help you find local groups interested in AI. Attending these meetings (often found in virtual formats as well) can help you network and learn from others in your area.
Participating in Hackathons and Competitions: Participating in hackathons or AI competitions like those on Kaggle can improve your skills and help you network with other AI enthusiasts and professionals.
Social Media: Join AI groups on LinkedIn, follow AI researchers on Twitter, or subscribe to AI channels on YouTube. These can provide you with a wealth of information and resources and the chance to interact with AI practitioners worldwide.
When engaging with any community, it’s essential to respect community guidelines, be polite, and give back to the community by answering others’ questions when possible. The spirit of these communities is mutual learning and growth, so your contributions are just as valuable as what you learn from others.
Remember, the best investment you can make in your AI journey is not necessarily financial. It’s the time you put into learning, practising, and experimenting.
Refrain from letting FOMO or scare tactics make you lose sight of the bigger picture. After all, AI is about innovation, discovery, and growth—not just a price tag.
Amol has helped catalyse business growth with his strategic & data-driven methodologies. With a decade of experience in the field of marketing, he has donned multiple hats, from channel optimization, data analytics and creative brand positioning to growth engineering and sales.