Steps to getting certified in AI

Become certified in artificial intelligence (AI) or any relevant field is an undeniably essential element in becoming a qualified AI practitioner. With the credential earned, you could also start working on your pet projects or even get the job you have always wanted.

How do you become an AI engineer? Wish to advance your career in AI but not sure how to? Is being an AI engineer a role that you have always wanted? If this is so, read on. We at CertifAI can help you become a top-notch AI engineer.

A brief introduction to AI before getting certified

Before knowing the steps to getting certified in AI, let’s have a quick view of what AI is! 

Artificial Intelligence (AI) is a computer science study for developing software or machines that display human intelligence and is growing at a swift pace. To define A.I., it is the ability of a computer programme to learn and think. In other words, everything can be considered AI if it involves a programme doing something that we would normally think would rely on the intelligence of a human. Nevertheless, it is one of the emerging technologies which tries to simulate human reasoning in AI systems. 

Back in the past, John McCarthy invented the term “Artificial Intelligence” in the year 1950. In the present, AI has become one of the trendiest topics in the world. According to studies, artificial intelligence will seep into your daily lives by 2025. Soon, AI jobs would be among the fastest-growing jobs in the technological sector, creating additional career prospects for those with the right AI skills. 

In addition, the advantages of Artificial Intelligence applications are enormous. Above all, it can revolutionise any variety of industries like healthcare, automotive, manufacturing and more. According to PwC (2021), 62% of business executives believe that AI could lead to more effective decision making. Moreover, 67% agreed that AI could help boost labour productivity, for instances. 

So, the question is, how to become an AI engineer?

Get certified by CertifAI – Let us help you

CertifAI is a company under Skymind, that offers training courses and certifications in AI. Besides that, we help to identify your business pain points and source the best-fitting talents for you. 

Nevertheless, we also provide personalised training to AI talents and certify qualified talents to prepare them to work seamlessly with your team to run projects. Our focus is to assist AI talents to master the skill sets required to secure and advance their careers in AI.

In this article, we would like to show you steps to getting certified in AI by CertifAI. Without hesitating, scroll down for more!

Step 1: Enrolment

First of all, join our Deep Learning in Computer Vision course, developed by CertifAI’s Deep Learning engineers who are qualified and experienced in this field. 

In this course, you will be exposed to how robots and machines “see”, learn, and respond from their environment. Furthermore, you will be taught on different underlying Deep Learning techniques used in Computer Vision systems. For examples, Object Detection, Image Segmentation, Conventional Neural Network, Deeplearning4j (DL4J) and a few more. 

Are you looking for a course that prepares you to become a deep learning engineer, AI tech lead, data scientist, or any relevant role? If yes, this course is the best option for you. Join now by completing the enrolment form. After that, wait for our representative to contact you!

Step 2: Test and Interview

Once registered, you have to sit for the Training Entrance Test (TET) hosts on the academy’s website. The test aims to screen your level of proficiency in the skillsets needed for the training.

Other than that, you will also need to sit for a coding assessment to assess your coding skills, in which only those who pass the tests will be able to join the training programme. Find out more about the course requirements here.

Step 3: Attend a 12-Day Training Course

After successfully passing the assessment and interview, attend a training course based on the dates as arranged. This course includes a total of 9 modules and takes about 12 days to complete. If you are interested, find out the course outline here

Upon completion, you would have learned useful technical skills on how to build advanced deep learning models such as YOLO, Conventional Neural Network and others.

As a reward, a certificate of completion will be distributed to all trainees. However, this does not indicate that you are qualified as an AI engineer. To become a Certified Engineer in Computer Vision, you will need to sit for a post-training examination a few days after the training.

Step 4: Sit For The Post-Training Examination

Last but not least, sit for the exam once you have completed the training course. The post-training assessment will test your skills and knowledge in Computer Vision using Deep Learning approaches.

Moreover, you would need to complete two sections of the test, which are the theory part and the practical or coding part. To know more about the exam and format, click here

Step 5: You’re Certified

After all, you are certified. Congratulations! Once you have successfully passed the Deep Learning in Computer Vision Exam, you will receive the certification of achievement or achievement with excellence. Hence, you are now qualified as a certified engineer in computer vision. 

With the accreditation earned, it is time for you to advance your AI careers in deep learning and computer vision fields. For examples, you could pursue job roles such as AI Engineer, Machine or Deep Learning Engineer, AI Tech Lead, Data Scientist and others. Visit here to learn more about the certificates that we offer. 

Easy-peasy!

“The journey of a thousand miles begins with one step.” – Lao Tzu 

Therefore, do not wait for more. Take your first step and join us today. We are excited to assist you throughout your journey into the AI space. For further questions, please feel free to reach out to us at our help and support hub

Deep Learning in Computer Vision

Enroll now to learn different techniques of Deep Learning in Computer Vision (image processing, image classification, object detection, and so on). 

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