How To Pursue a Career in Artificial Intelligence

May 16, 2021

7 Min Read

The word “Artificial Intelligence” is often associated with new generation technologies like smart assistants, robotics, chatbots, and autonomous cars. Unlike some technologies that were introduced with tremendous potential, only to fade away over time, artificial intelligence (AI) innovations have reached beyond the realm of imagination. Artificial intelligence has immensely contributed to transforming our lives with advanced technologies that have made our lives simpler. From automation of laborious tasks, aiding in medical imaging, detecting suspicious activities in finance to smart driving assistance, AI is playing a part in every single industry.

As observed by experts, the recent trends suggest that AI is expected to witness a growth rate beyond the expected margins. The AI industry is likely to transform into a staggering $3.9 trillion industry by 2022. With such statistics, you must wonder about getting into an exciting industry as there are ample job opportunities and career growth. The acceptance of AI in every possible industry has caused an increase in the number of jobs listed every year. To relish these opportunities, you must be equipped with the right skills and follow the right career path to climb the AI industry ladder. The article delves into the critical aspects of building an AI career.

How to Begin in the AI industry?

Artificial Intelligence and Its Subsets

For all the aspiring AI professionals out there, it is crucial to understand the concepts of artificial intelligence and its subsets to have comprehensive knowledge about the field. You might have come across the terms AI and machine learning that are mentioned together; however, the definition varies and the role it plays in the IT industry. AI is a part of the computer science domain capable of performing similar to a human mind. The motivation of AI and its architecture is derived from the human brain that comprises neurons and how it processes information. AI systems enable automation and perform complex operations efficiently and much faster than an individual.

Machine learning is among the subsets of artificial intelligence. The machine learning algorithms empower computer programs with the ability to learn and train to achieve accurate predictions. Ideally, the machine learning algorithms are trained with datasets to learn how to make the decisions to achieve the desired result.

Are you wondering where deep learning fits in? Deep learning is a subset of machine learning. It can be rightly termed as the evolution of machine learning to achieve the results. As earlier mentioned, AI is inspired by the human brain and is depicted more in deep learning approaches. Deep learning architecture and algorithms are based on the human brain’s functionalities for pattern recognition, image detection, classification, identification, and processing of information. The human brain’s deciphering process has been replicated in the deep learning approaches to achieve the targeted outcome. Some examples of deep learning algorithms are artificial neural networks (ANN) and convolutional neural networks (CNN). Before we proceed to the next set of information, it is crucial to highlight a fundamental difference between machine learning and deep learning.  While deep learning can discover the features needed to process the information for performing a certain task such as classification, machine learning algorithms require you to feed in the data to perform tasks.

Educational Qualifications

To get started in AI, it is of utmost importance to hold a higher educational degree, namely, a bachelor’s in mathematics and computer science. Additionally, the knowledge of statistics is a plus. If you aim for positions higher up the hierarchy, then a master’s degree or a doctorate ensures supervisory and leadership roles in the organization. Apart from higher education, you must have coding proficiency, analytical skills, communication, and problem-solving skills. As the field is continually developing, it is advisable to stay updated with the recent developments, newer skill sets, new additions in terms of programming languages and tools. This has been possible with the online platform offering courses in collaboration with top-tier universities and organizations.

Technical Know-How

Programming Languages

For attaining a successful career in artificial intelligence, you must be aware of programming languages with the likes of Java, Python, R, C++, MATLAB. There are several upcoming trends and updates in terms of programming languages. Therefore you should stay up-to-date in the IT industry. Another critical aspect is to have proficiency in more than one programming language, as the idea of a single programming language is not considered valuable among the experts, thus the percentage of hiring or getting into a higher role diminishes.

Neural Network / Deep Learning and NLP

Perhaps, the most critical concept in AI is neural networks. It is essential to equip the knowledge of neural networks and their types. Today, neural networks are considered the core behind every successful AI model that is implemented. Neural networks can perform complex problems across disciplines. Some of the problems where AI has succeeded are classification tasks, image processing, machine translation, conversational AI. The architecture of a neural network imitates the human brain and its functions of processing information.

Deep learning is considered an advancement to machine learning technologies. Although a subset of machine learning, deep learning techniques can improve the performance of machine learning models. The need for human supervision is lesser in deep-learning powered tasks as opposed to machine learning techniques. Deep learning experts are among the most sought after professionals today. The use of deep learning has increased in research, thereby increasing the demand for such experts.

Have you wondered what the technology is behind Google Translate or Search engines? It is natural language processing (NLP). With this subfield’s ability and its growing usage in the field of text and speech recognition, NLP has gained immense popularity. The ability to read, interpret, or understand human language and interactions with humans through intelligent systems is possible with NLP. The technologies incorporated with NLP at its core are used by search engines, writing assistance tools, voice assistants, to name a few.

 Soft Skills

To have a supervisory role that is higher up the pecking order, you must be skillful with your communication abilities. To put forward your ideas to the top-authorities, researchers, and stakeholders, interpersonal skills and analytical capabilities sit higher up in your skill repository. Another aspect of soft skills that you should be aiming to develop is your creativity, active learning to stay updated, judgment, and decision making.

Additional Tips to Get Started

For all the beginners fascinated by the field of artificial intelligence but don’t know where to begin, here are some additional tips for a head start for your career to gain entry into the field of AI.

Higher Degrees

If you are a graduate and looking for a career in AI, you should be aware that you might be getting into entry-level positions with a bachelor’s degree, and with experience under your belt, you can climb your way up. There are several Master’s degree programs from notable universities with a specialization in AI in recent times. A higher degree will ensure to get into competitive and lucrative roles.

Online Certification

The people who have started a career but willing to switch should aim to opt for online certification programs. Although it is tricky to pick the right course, you will benefit from a certification course offered by top-tier institutions. A key aspect to look for is the hands-on experience. It is advisable to opt for a course that provides the right balance between theory and hands-on experience.

Develop a Solid Portfolio

Beginners and professionals need to understand the need for a strong portfolio to be considered employable. The lack of a portfolio that identifies how many projects you have worked upon determines your expertise with programming and real-world problem-solving capabilities in a competitive market. Opting for guided projects on online platforms, working on capstone projects across multiple disciplines, and participation in challenges offered by Kaggle and similar platforms will enable you to gain proficiency and experience to handle projects with large datasets and complex problem-solving expertise.

Job Roles in the AI industry

  • Data Scientist
  • Machine Learning Engineer
  • Artificial intelligence Researcher
  • Data Engineer
  • AI Architect
  • NLP Experts

Top Online Courses to learn AI

AI for Everyone (Coursera)

The course introduces the concepts of AI and its terminologies. You will have an in-depth idea of a neural network, machine learning, deep learning, and data science. What AI can realistically do and cannot do, the opportunities, building machine learning models for projects, and starting with an AI team are some of the concepts covered in the course. It is a beginner level course.

Post-Graduate Program in AI and Machine Learning (Purdue University: Simplilearn)

The certification program explores the concepts of statistics, python, machine learning, and deep learning networks. The concepts of NLP and reinforcement learning are covered in detail and you will be able to build and deploy deep learning models on the cloud.

IBM AI Engineering Professional Certificate

This certificate course is offered on Coursera. You will learn machine learning, deep learning, neural network, and machine learning algorithms such as classification, regression, clustering, and dimensional reduction. Additionally, you can learn to deploy the machine learning algorithms and pipelines on Apache Spark. The course is an intermediate level course.

Machine Learning by Stanford

The course is offered on the Coursera platform. It is designed and offered by Stanford University. The curriculum covers the entire machine learning concepts and algorithms thoroughly with hands-on experience in the course as well.

Computer Science for Artificial Intelligence (Harvard University: edX)

The course introduces graph search algorithms, reinforcement learning, machine learning, artificial intelligence principles, designing intelligent systems, and python programs for AI are some of the course’s actual contents.


Artificial intelligence is playing a vital role in the recent innovations that we witness today. The role of AI is continuously increasing across several industries. From the world of academia to healthcare and business, AI is everywhere. With automation capabilities and the lack of supervision needed for AI systems, the industries have achieved efficiency and faster outcomes of complex problems. AI is also playing a critical factor in reducing cost by cutting down the time required for laborious tasks to take days to finish. A higher degree and a professional certification course from top institutions give you industrial recognition and practical knowledge for challenging tasks. The careers in AI are lucrative and peak with massive demand in the job market today. It is worth investing in this career as it is sure to boost your professional growth and achieve new heights.


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