Training programs on generative AI are now popping up everywhere. One of my connections called it “a joke” after enrolling in such a program. Many are expensive, time consuming and offer low value. Participants proudly display the certification and even praise the instructors. They don’t know that in the end, it won’t lead to any job offer. Many programs simply exploit the ignorance of the general public. Sometimes because the operators and instructors genuinely believe that they are expert in generative AI.
But how would you expect to learn even the foundations of generative AI if you can enroll without real coding or machine learning experience? In this article, I discuss serious technical training. I rule out general courses comparable to cooking lessons for amateurs. Thus I focus on training advertised as technical, and sometimes described as advanced.
The Academic Model
Academia typically offers some of the most expensive programs. There are two reasons for this. First, higher education capitalizes on large loans that students can get to pay for the tuition. Then, the fact that many universities are still respected by employers. Typically, out-of-state students must pay more. However, much of the money collected — in US at least — goes to administrators and sport-related investments. Instructors get little money. Indeed, in many cases, the majority are part-time adjuncts paid very little, not having a PhD, or are inexperienced PhD students.
Also, due to the tenure system, many of the courses are outdated. Some feature old methods such as linear regression and rename them as statistical machine learning. Or focus on poor concepts and old jargon such as p-value. But the content hasn’t changed for decades. Reading the textbook is like an excursion back in time. Yet, in many curricula, it constitutes the bulk of the training. Sometimes the program contains a session or two to modernize the presentation. For instance, students may run a deep neural network (much simpler than GAN), or play with bags of words. The latter is described as NLP or even LLM in the advertising material. In reality, projects, even when involving web crawling, are of limited scope. You are unlikely to learn smart techniques for crawling and organizing unstructured tex.
Your best bet is to look for programs where external practitioners from top companies actually teach most of the classes. Universities that produced many successful entrepreneurs or attract private funding, usually do that.
Strange Selection Process
As if this was not enough, you may have to deal with a lot of paperwork. It starts with filling up long questionnaires and pay a non-refundable fee. You may have to take tests that are irrelevant to the target certification. And typically, provide GPA scores and answer questions about your race, gender, disabilities or even sexual orientation or vaccinations, and possibly send recommendation letters. I am not sure how this relates to your qualifications for learning generative AI. In my case, I don’t have any GPA and if I had a score, it would be from 30 years ago. Which state you live in is an important issue that may require official documentation. Surprisingly though, you don’t need to show your GitHub portfolio, as if this was irrelevant.
The Data Camp and MOOC Models
MOOC — massive open online courses such as Coursera — and intensive data camps range from cheap to very expensive. The quality is not always correlated with the value that you get. We can exclude those that won’t even name the instructors: avoid them. Even then, the common drawbacks are similar. In many cases, they treat participants as if they know very little. Possibly because they try to attract as many people as possible, to boost revenue. The admission bar may be lower than stated in the marketing material.
The end result is as follows. You spend long hours learning the basics that you already master. Videos are progressing painfully slowly. Frequently, for several minutes, the instructor enters Python code on a Jupyter notebook, one character at a time. This would put me to sleep. Then, the exams consist of multiple-choice questions. It does not measure your ability to work in a professional environment. Sometimes it involves judgmental questions and the “correct” answer is questionable. However, you can typically preview free material and judge by yourself whether it is worth your time and money. Beware of programs that guarantee job placement or refund. You may not end up getting the job you were hoping for. Unfortunately, if you are a novice, you might not be able to judge the value of the program.
One way to assess the value is to contact people featuring the certification in question on their LinkedIn profile page, and ask them what they think about it.
How People Learn
People learn very differently. There is no “one size fits all”. If after your due diligence, you found a program that meets your needs and budget, go for it. Beware of time commitment. Is spending weeks on a program really worth it (even if free), compared to using your time for other purposes?
In my case, I learn by practicing on my own, finding answers to my questions on Stack Exchange and other discussion forums, even asking my own questions. I find data sets on Kaggle and other places. I learned TensorFlow and GAN (generative adversarial networks) by testing and improving Python code that I found on Machine Learning Mastery and Medium. Now you can find much better versions on my own platform! On occasion, I pay someone to develop and deploy some applications. Most recently, to design a Web API with Python back-end for one of my GenAI apps. The next project on my list will be about designing a SDK. Little by little, I am becoming more of an engineer. Interacting with the people that I hired was very fruitful, less expensive than most courses, and a lot faster. And of course, highly targeted to my “job”. But that’s just me.
Alternative Model
Since I could not find any training that suits me, I eventually decided to offer my own. I realized that I was not the only one. Many professionals with limited time, who know already more than the basics, are in the same situation. Like myself, they can learn a lot on their own and only need limited guidance, essentially to get jump-started. I created my program for them. Participants typically have a minimum technical background and know Python. For instance, engineers and product people in various industries.
If you are curious, you can check out the certifications from my AI/ML research lab, here. The one on generative AI is based on my book now accepted by Elsevier. The cost is lower than the admission fee for any academic program. The value is superior, especially due to the depth and novelty of the material offered, as well as customization. And time commitment is smaller by several orders of magnitude, thanks to focusing on the most valuable aspects, going straight to the goal, without boring videos.
About the Author
Vincent Granville is a pioneering data scientist and machine learning expert, founder of MLTechniques.com and co-founder of Data Science Central (acquired by TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS).
Vincent published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is also the author of “Intuitive Machine Learning and Explainable AI”, available here. He lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.
