3 Effective Ways to Become a Computer Vision Engineer

Are you looking to delve into the world of technology and automation? Are you interested in learning all about computer vision but don’t know how to become a computer vision engineer?
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Are you looking to delve into the world of technology and automation? Are you interested in learning all about computer vision but don’t know how to become a computer vision engineer?

What is a computer vision engineer?

Computer vision is one of the fastest-growing fields of artificial intelligence that’s mainly concerned with improving the ability of machines/computers to read, analyze, and process digital image labeling content (video or images). Computer vision engineering is more precisely an interdisciplinary field that is at the forefront of today’s technological advancement; looking to automate the human visual system. The core responsibility of computer vision engineers is to create complex algorithms that assist machines in reading, analyzing, processing, and extracting desired information from digital AI image processing. The field is closely related to AI, and machine learning technology, with a similar scope of enhancing the capabilities of machines to achieve a higher level of automation for various tasks.

How to become a great computer vision engineer?

Well, as an interdisciplinary field, you need to be good at multiple fields to become a truly great computer vision engineer. The key is to get knowledge about various related disciplines including programming languages, deep learning, machine learning, statistics, and others. Well, if you too are looking for answers about how to become a computer vision engineer; continue to read on as we list down some great resources that can help you gather much knowledge and command over the computer vision field.

  1. Massive Open Online Courses (MOOCs)

Massive open online courses (MOOCs) are certainly one of the easiest and most approachable ways to get insights and knowledge about computer vision. The MOOCs were introduced as a learning tool back in 2008. Today, it is considered one of the most authentic and resourceful too for data scientists and machine learning practitioners to achieve a higher level of domain expertise. Today, many open online courses are accompanied by certifications and accreditations from leading academic institutes.

Now, since we are talking about how to become a great computer vision engineer, there are various reasons why MOOCs are considered the best resource. For once, MOOCs offer the most updated content. Remember, computer vision like many other disruptive technologies is a fast-evolving field. This means that the content that you learned a year back may not be all that relevant today. With MOOCs, you get to leverage the best and most advanced content for computer vision engineers. This helps you to achieve better expertise in the domain. Also, MOOCs are considered the most affordable learning approach as compared to traditional academic institutions. In addition, the flexibility to take courses at your own pace makes MOOCs the best tool to master the domain of computer vision engineering.

Here are links to some MOOCs below:

  1. Certifications

Now, here we don’t mean certifications earned through online courses, rather other specialized certifications reflect the expertise of computer vision engineers in certain frameworks or libraries.

TensorFlow, GCO, and AWS are some of the tools that can help gain significant domain expertise for computer vision engineers. Also, all of the companies involved in the development of these tools offer recognition certificates, which demonstrate the strengths and expertise of individuals in specific domains.

An advanced academic degree coupled with a portfolio was considered sufficient to grab an ML role a few years back. Today, there is increased demand by recruiters and employers for specialized certifications as a demonstration of expertise.

Here are some worthy certifications that you can invest as ML practitioner;

  1. Programming Languages

In today’s digital world, any level of expertise in a programming language is considered an asset. This becomes even more important for all software engineering professions including computer vision engineers.

Your query about how to become a computer vision engineer starts with gaining expertise in programming languages. At the very basic, you need to develop your expertise in Python and R programming languages. These two are the most demanded programming languages in all sorts of machine learning or computer vision engineer roles.

Having at least intermediate expertise in Python, and R languages will help computer vision engineers to understand and implement deep learning models; widely used in different environments.


Computer vision is one of the fastest evolving fields of AI with the demand only expected to increase as AI makes its way into various business and everyday applications including AI drones, AI image processing, and others.

As with other technological tools, computer vision evolves at the pace of innovation and you need to continuously improve your understanding and expertise of the latest tools to become a great computer vision engineer.

Above, we have presented some of the best approaches to consistently improve your expertise in the computer vision domain and become an expert computer vision engineer.