Artificial Intelligence (AI), a computer science study for developing software or machines that display human intelligence and is growing at a swift pace. Whether it’s choosing your news feed, Google search results, or Facebook algorithms, AI is doing its job with perfection. In a nutshell, AI development is the science of machines that can find solutions to crises as a human would. From search engines to self-driving cars, AI has been at the center of some of the most incredible projects. According to studies, artificial intelligence will seep into your daily lives by 2025, and Artificial Intelligence jobs would be among the fastest-growing jobs in the technological sector, creating additional career prospects for those with the right AI skills. AI Engineers’ salaries careers are among the best in the world. So, the question is, how to become an AI engineer?
Artificial Intelligence Engineers, AIE are people involved in problem-solving or Artificial Intelligence technology. They create, test, and implement AI models, along with the handling of the AI infrastructure. They cruise through traditional software models and Machine Learning models. With the development of Artificial Intelligence and Machine Learning in different areas of the IT industry, the demand for these IT professionals is high.
To succeed in Artificial Intelligence jobs, you need to have an analytical thought process and the ability to solve problems with cost-effective, efficient solutions. AI engineers also need to have insight into technological innovations that translate to state-of-the-art programs. The following topics provide an insight into the prerequisites that you must acquire to get the AI job of your dreams.
Let’s start with the basics. You go with the bachelor’s degree route first. You can earn a degree in either of the following subjects to be an AI engineer: Computer Science, Mathematics, Information Technology, Statistics, Finance, and Economics. An AI certification path could do very much the same cheaper and in less time.
If you are researching how to become an AI engineer, you need to up your software development techniques and practices, along with your programming skills. Make sure that you are theoretically and practically well- informed in the following topics:
You need to have a very good grasp of programming languages, such as Python, R, Java, C++, and so on. It’s critical to have a robust understanding of classes and data structures. At times, just the knowledge of these technologies may not suffice. You might come across projects where you need to leverage hardware knowledge for enhancements. You must be familiar with basic algorithms, classes, memory management, and linking.
Coming to statistical knowledge, you will need to be intimately familiar with matrices, vectors, and matrix multiplication. If you have an understanding of derivatives and integrals, you should be in clear. Statistics are going to come up a lot. At least make sure you are familiar with Gaussian distributions, means, and standard deviations. Areas, where you will be frequently using your algorithms and applied mathematics knowledge, are:
To be a successful AI engineer, you must have in-depth knowledge of algorithm theory and how algorithms work. All this math might seem intimidating at first if you’ve been away from it for a while. Be prepared, Machine Learning and Artificial Intelligence are much more math-intensive than something like front-end development.
Natural Language Processing combines two of the major areas of Machine Learning and Artificial Intelligence: Linguistics and Computer Science. The possibility of you working with either text or audio or video is very high. Therefore, as an AI Engineer, you have to work extensively on NLP, which involves language, audio, and video processing by leveraging various NLP libraries and tools, including:
At times, we might need Machine Learning for tasks that are too complex for humans to code directly. This is where neural networks come in. Neural networks are modeled after the human brain, which can recognize numerical patterns based on sensory data.
The Artificial Intelligence world has naturally progressed from single-layer neural networks to Deep Learning neural networks, in which data is passed through multiple layers for more complex pattern recognition. Deep neural networks have been by far the most accurate way of approaching complex problems, like Translation, Speech Recognition, and Image Classification, which play a very important role in AI.
In order to secure an artificial intelligence job, you need to possess certain business skills along with your technical skills. The following are some of the business skills required to be a successful AI engineer:
You can accomplish the all above-mentioned skills through practice or by choosing a certification course. To be an AI engineer, completing a certification course in Data Science, Machine Learning, or Artificial Intelligence is highly recommended. These certifications will add value to your resume and will help you to acquire in-depth knowledge of AI topics, along with hiking up your pay to match an AI Engineer’s salary.
As the domain of AI is new, there are still many sectors that have not yet used AI. So, before the ‘how’ part, let us briefly squint to the ‘why’ part. Let us see the trends that Gartner predicts about Artificial Intelligence:
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