From voice-controlled assistants to autonomous cars, artificial intelligence (AI) is everywhere. It is fair to say that AI has become an all pervasive technology. And it is a really rewarding experience to stay on the makers’ side of the table. If you love mathematics, if you are made for coding, you will love working with AI. There are many reasons why you should join the AI bandwagon by getting an AI courses and certification and the awesome pay-packages that start from Rs 12L per annum in India is just one of them. But instead of talking about why you should learn AI, let us just talk about how you can prepare for a career in this field. Let us create a roadmap for you.
The foundation
You have to know the mathematics that run under the hood of AI. That means understanding what the code does underneath, and why you use certain functions. Ask yourself if you have a solid grasp over statistics, linear algebra, and calculus. If not, it is time you sat back and spent some time on those. You will need the understanding of statistical models and a fair hold over probability.
Can you code?
The better you are at coding the easier your life is going to be as an AI professional. The most popular programming language among the machine learners and deep learners is Python, followed by Java and Julia. If you are new to coding, it is a good idea to start with Python given its simple syntax, tolerance to error (although a lot of people think of this as more of a weakness, it leaves room for bugs), and moderate learning curve.
Conceptualize deep learning
AI as we see it today is the result of the strides made in deep learning. Deep learning, which is essentially a form of machine learning that uses artificial neural networks, is the engineers’ way of mimicking the animal brain. The goal is to help a machine learn, think, and decide based on data.
You will be doing most of your work around deep learning and it is advisable that you solidify your grasp on the principles of deep learning and then go on to learn about the deep learning frameworks like Tensorflow, Keras, or Pytorch.
Master different modes of machine learning
The machine learning algorithms are usually classified as supervised learning, unsupervised learning, and reinforcement learning. Each has its own specific purposes. For instance, the idea of the self driving car is founded upon reinforcement learning where the AI is perfected with the help of a reward and punishment based model. These modes of machine learning apply to the training of neural networks and thereby to deep learning. The more comfortable you are with the different algorithms and their usage in different situation the better for you.
Study other people’s work
A lot of innovation and thought goes into the formation of AI, and you cannot go by the book all the time. People have been working on AI for decades, and it would help if you took the time to study a bit of what others have done.
