Learning AI/ML: Does the Institute Really Matter?

Artificial Intelligence (AI)/Machine Learning (ML) has reached a tipping point in the last two-three years. While the conceptual work on these areas may have started many years back, these technologies’ commercial and application dimensions have manifested in a big way only recently. No wonder there is so much interest in this field.

Artificial Intelligence is redefining business models and customer experiences in a variety of industries. Thanks to the use of these forces, almost every industry – from banking to retail, from healthcare to oil exploration, from manufacturing to utilities – is experiencing a paradigm shift. The AI as a service market size is growing to be as big as $10.88 billion by 2023. This would expand further demand for the AI talent pool

More AI jobs, more courses, more confusion

A massive AI push translates into a lot of demand for skilled AI and ML professionals. Yes, many courses and institutes have also mushroomed all over to fill the demand-supply gap as quickly as possible. If you aspire to leverage opportunities in this booming industry, you can join any of these courses. Wait! ‘Any’ of these courses? Or does it have to be well-considered and carefully weighed in choice?

Institute you choose will have a profound impact on your skill’s quality and shelf-life. This would matter a lot considering some specific dimensions relevant to a technology area like AI and ML.

  1. AI is all about fundamentals. ML is all about algorithms. If the course you pick is deficient in equipping you strongly with conceptual areas, models, and mathematical aspects, there is not much use in doing such a course. More often than not, a student would only cover the technology in a superficial sense. The core understanding and grip on AI and ML would be conspicuously missing if the conceptual framework is not taken care of.
  2. Interestingly, these technologies are still going through a choppy curve of adoption. The major reasons for this hesitation and skepticism are ethical implications and uncertainty. AI is mighty, and the same power renders it equally positive and dangerous, too, depending on what hands are using it. So the course instructor and experts have to be well-versed in explaining these aspects of AI to the learners. This cognizance of the big debate and integrity part of AI will only develop when AI is learned under responsible and aware people.
  3. AI and ML are dynamic technologies. They are still evolving, changing, adapting, and re-orienting every day. Some new research or update happens all the time, changing the contours of these technologies. A course with an in-depth and collaborative link to academic experts would be the right course to opt for. Such a course could update and edify its curriculum with no lag or confusion. Also, the presence of able academic thinkers would endow the course with the requisite depth.
  4. AI courses with the last-mile connection to apt industry ecosystems would allow a learner to master the applicability parts with a definite sense of confidence. This is where a lot of experimentation, trial-and-error practice-based learning, and exposure would be available to the participants.

Look out for courses where the curriculum is designed with a conceptual but contemporary framework. The linkages to academic veterans and industry practitioners are available in a balanced way.

That will allow you to learn AI with a long-term and responsible perspective.