Starting from Scratch: AI

With the growing prevalence of AI transforming the way industries operate, there’s no better time than now to learn more about it. But where do you start?
The gradsingapore Team
Dawn Yip
Senior Writer
Starting from Scratch: AI

It’s hard to ignore the ever-growing presence of AI in today’s workforce, especially in recent years. Once considered a novelty for tech giants, it’s now considered mainstream technology, especially with the launch of generative AI (GenAI) in 2023. Tools like ChatGPT and Midjourney are now rapidly being adopted and utilised by companies from a variety of industries; a report by McKinsey notably found that a third of organisations already use it regularly for at least one function at work.

With the world transitioning to digitalisation and technology advancing swiftly, there’s no better time to get into it yourself. After all, AI and machine specialists are now topping the list of fast-growing jobs over the next five years. This is reflective of the demand for AI skills that will only continue to grow alongside the number of related jobs. Plus, this field promises a dynamic and fast-changing environment that’s ideal for those who enjoy a good challenge. 

But then comes the next question: where should you start?  

The road to learning

Contrary to popular belief, picking up AI skills isn’t necessarily as difficult as people chalk it up to be. However, that doesn’t mean it’s a cakewalk either; the road to mastery does come with its own set of roadblocks to overcome, and depending on your background, the amount of time taken will vary.

Make some plans

Before you even get started, it’s probably a good idea to establish a learning plan for yourself. This should include a tentative but flexible timeline with objectives to be met, as well as what courses, activities and resources you’ll need to achieve them.
Don’t feel like you need to have everything planned out, though. Rather, try breaking the learning process down instead of taking the material in bulk, while focusing on a small number of manageable goals. For example, you can start off by focusing on mastering the basics before jumping into more advanced topics. 


Understand the basics

Foundations are important when it comes to skill-building, and AI is no exception. Sure, you could try going in blind, but it’ll be an uphill battle for you to grasp anything without the essentials. On the contrary, it’ll make learning more advanced concepts much easier if you master the basics, and you’ll feel more confident during the learning process.
However, keep in mind that the knowledge base you’ll be building will vary depending on the role or skill you want. For example, a data scientist doesn’t need to know every mathematical concept in AI, but a research scientist will!


Pick up some programming skills and principles

The next step is to take on the tangible skills needed for AI. Just like the previous step, the level of studying you’ll need will vary depending on what role you’re pursuing. Here are a few for example:

Programming: Unsurprisingly, knowing how to code is a critical part of working with AI, such as developing models, algorithms and more. While Python is a common (and the most popular) choice due to its simplicity and adaptability, there are also other coding languages you can pick up, such as Java and C#, that operate with more specific purposes and platforms in mind. 

Data structures and manipulation: Known as specialised formats to store, retrieve and manipulate data, data structures are vital for writing efficient code and in turn, developing complex AI algorithms. 

Data science: The study of data to extract useful insights, it utilises a variety of principles and practices from a variety of disciplines, such as mathematics, statistics and computer engineering.

Machine and Deep Learning: Considered a core part of AI, machine learning (ML) draws from data to make predictions and improve a product’s performance. On the other hand, deep learning is a subset of ML that utilises layers of neural networks to understand patterns in data, allowing you to build more advanced models.

These skills generally interconnect with one another, and will play a big role in strengthening your base of AI concepts. Once your foundations are established, you’ll be more prepared to dive into more complex topics or even start the path to specialisation.


Keep keeping up

Once you’ve gotten the ball rolling, the next steps are up to you. But regardless of the path you choose, keep learning and improving on your skills. The world of AI has been (and will always will be) evolving, with new developments capable of shaking up the field. Not only does this make the industry exciting and filled with potential opportunities, but you’re also unlikely to run out of new, unique things to learn about.

So commit to your goals and stay updated; follow AI blogs, take up more advanced classes and keep challenging yourself. The journey might be tough, but with time, effort and perseverance, you’ll gradually be able to graduate from a novice to an expert.