Becoming a Successful Artificial Intelligence Engineer: Skills Needed and Relevant Courses to take.

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The ability of computers to learn and perform tasks is no longer the stuff of sci-fi movies. Artificial intelligence has become increasingly popular in business circles and has brought with it reputable and marketable jobs for anyone with programming, computer science, statistics, and advanced data skills. For instance, artificial intelligence engineers are behind most of the deep learning infrastructure necessary for any computer to learn patterns from data and perform skills such as language recognition, play a game of chess, or in the case of cars, self-drive.

Who are AI engineers and why do employers need them

Figure 1: The levels of AI (Image Source: Kdnuggets.com)

Many companies, large and small, have delved into the different levels of AI. SAS® for instance,

  • Provides vehicle manufacturers with sensors that make cars more interactive and responsive to human needs, resulting in a safer driving experience.
  • SAS also collect biometric data through devices worn by patients, and this allows for physician care from home.  

As long as digital technology continues advancing and more people continue gaining access to said technology, AI will continue being relevant today and in the future.

Opportunities for Artificial Intelligence engineers

“Every aspect of our lives will be transformed [by AI],” and it could be “the biggest event in the history of our civilization.” Stephen Hawking

It is very likely that as the tide of AI and machine learning continues to grow, companies that will not adapt will be left behind.

Forecasts by International Data Corporation (IDC) estimate that AI spending will reach $57.6 billion in 2021. The years 2016-2021 will experience 50.1% compound annual growth rate (CAGR), owing to aggressive investments in AI solutions across multiple industries.

As the growth continues, enterprise software, hardware, and use cases will continue to grow. Customer service automation and healthcare diagnostic systems will continue to be the most common use cases over the five years.  

The report gave an industry-specific breakdown as follows:

  • Retail, healthcare, banking, discrete manufacturing, and process manufacturing will account for 55% of the forecasted spend. In addition, the CAGR for retail is expected to be the highest, at 58.8%.
  • Increased interest in intelligent processing automation will attract more investment for the area, to make it one of the top three biggest use cases by 2021. On the contrary, spending in automated preventative maintenance will fall during the same period.
  • Product recommendations, shopping advisors, emergency response, and public safety are other use cases that are expected to experience the highest spending growth year on year.
  • Software will be the beneficiary of about half of the forecasted spend. This includes the development of cognitive applications and platforms

The roles and responsibilities of an AI engineer

The role of Artificial Intelligence engineers depends on their area of specialization.

  • Research scientists design, collect and analyze data
  • Software engineers create programs, applications or systems, monitor their performance and modify them as needed, to respond to business needs.
  • C# developers ensure that programs and applications are scalable, secure and perform well.
  • Software development managers are in charge of projects and oversee tasks such as software design, testing, installation, and system maintenance.
  • Java programmers ensure proper integration of java into applications, websites and software.
  • Software Analysts audit software performance and recommend modifications or new developments based on need.

Below are a few job postings to give you a more in-depth view of what specific employers may be looking for.

Becoming an Artificial Intelligence Engineer

Skills required and how to develop those skills

To become a successful AI engineer you need a mix of technical and “soft” skills. Most importantly, you must be patient and willing to learn, as a full AI course takes a couple of weeks.

Most companies recruit candidates with a bachelor’s degree for entry-level positions and master’s or doctorate degrees for leadership positions. An AI major is always an added plus. Recruiters also assess knowledge in areas such as:

  • Programming. While you could excel at AI engineering with any programming language, python is the most recommended and is also widely used.
  • Robotics
  • Physics, Engineering and mathematics algorithms, statistics, probability, logic
  • Coding and programming languages
  • Graphical modeling
  • Cognitive science. This borrows from different fields and promotes an understanding of how mental processes work.

Other abilities that will make recruiters consider you for top AI jobs include:

  • A systematic and analytical approach to thinking.
  • Problem-solving mentality. You should have the foresight to conceptualize how solutions can increase efficiency, cost-effectiveness and add competitive advantage.
  • Great communicator and team player who can translate technical aspects to an environment full of non-technical employees.
  • Ability to translate digital knowledge into products that can benefit human beings.

With these skills, you can work in industries as diverse and different as Arts and military, as long as the company is interested in AI and is investing in its development.

Recommended courses for AI Engineers

Coursera courses on Artificial Intelligence

Deep Learning Specialization

Coursera offers several specializations (each specialization features several courses) that are geared to create mastery in a specific area. Specializations feature hands-on projects, which are necessary to get certified.

There are 5 Courses in this Specialization

  • Course 1: Neural Networks and Deep Learning
  • Course2: Improving deep neural networks: hyperparameter tuning, regularization, and optimization
  • Course3: Structuring machine learning projects
  • Course4: Convolutional neural networks
  • Course5: Sequence models

You can learn at your own pace, pausing and starting courses to suit your availability and progress. Top leaders in the industry also feature in the course, for talks and advice.

This course is Python-based, and all projects are conducted in TensorFlow. Familiarity with python is necessary, although python and tensor flow are also covered in the curriculum.

Cost: Available once you enroll. Financial aid offered.

Certification: Yes

Deep Learning for Business 

This beginner-level course is focused on strategy and is suitable for anyone involved in planning for products and services that are AI based. The course is structured into three parts:

In the first part, you will focus on creating a business strategy that is centered around deep learning and machine learning technology.

In the second part, you will dive deeper into neural networks, which are the core of deep learning systems.

In the third part, you will practice designing neural networks using tensorflow.

Students can learn at their own pace.  

Cost: free, but you purchase a certificate

Certification: Yes

Edx: Artificial Intelligence at Columbia University 

When you sign up to Edx, you will learn AI from accredited universities. With this course, you will gain a better understanding of the techniques necessary for creating intelligent computers with python, and how to use these systems to solve real-world problems. Among other things, you will also:

  • Learn linear regression
  • Code algorithms for machine learning
  • Learn applications of AI in areas such as robotics, language processing, and vision

The course, which is part of a MicroMasters program offered by Columbia University, is only open to residents of Massachusetts. Internships and full-time jobs are guaranteed to those who complete the course within a stated time period.

Cost: Content is free. Certificate Is $199

Certification: Yes

Artificial Intelligence Courses on Udacity

Udacity partners with companies and universities to offers one-off courses or nano degrees for larger courses. Some of their artificial courses include:

Intro to Artificial Intelligence 

Covering topics such as language processing, robotics, machine learning, among others, this course is an introduction to AI basics. It takes approximately 4 months to complete the course, at the end of which you should have intermediate skills.

Cost: Free

Certification: No

Knowledge-Based AI: Cognitive Systems by Georgia Tech 

If you want to learn how to create flying cars, start by learning this course. It is an excellent path towards the more specialized Flying Car and Autonomous Flight Engineer Nanodegree Program.

Offered by Georgia Tech, the course has these goals:

  • Teach you AI basics: concepts, problems and methods for creating solutions.  
  • Teach you how to apply concepts to the creation of AI agents.
  • To give you an understanding of the correlation between human cognition and AI.

In addition to learning from industry pros, you will gain access to interactive quizzes and rich content. You can learn at your own pace and if you get stuck, reach out to a huge support community.  

After roughly 2 months of taking this free course, you will have advanced AI skills in the focus areas.

Cost: Free

Certification: Unclear

Nvidia’s Fundamentals of Deep Learning for Computer Vision

Computer vision focuses on training computers to “see” in the same way that human beings do. The discipline teaches computers how to process visual data and classify it based on pattern recognition. Some problems in the world can be solved through object recognition and Nvidia, a graphics cards manufacturer, has played a key role in spearheading computer vision and its application to the real world.

You can learn at your own pace, and as part of your assessment, you will build and deploy a neural network.

Cost: $90

Certification: Yes

SimpliLearn’s AI Masters program training course

The Artificial Intelligence engineer course offered by SimpliLearn has three levels, all featuring a mix of instructor-led and self-paced learning and mentorship. All courses teach both the theoretical and practical aspects of AI. With this course, you will;  

  • Use tensorflow to interpret results and execute projects, including the implementation of different types of algorithms
  • Learn the heuristic and mathematical aspects of machine learning  
  • Get more from your data with a deeper understanding of neural networks and data abstracts
  • Explore AI use cases in different industries. You will work with over 5 real-life projects from different domains, to improve mastery of AI concepts like supervised and unsupervised learning, deep learning, neural networks, and predictive data modeling.  
  • Know how to take advantage of AI frameworks to categorize problems. For instance, a search problem, planning problem, etcetera
  • Find your area of AI specialization.
  • Learn how to make use of different analytics tools such as NumPy and Pandas, AI tools like Keras and Tensorflow and programming tools including python and Scala.

Cost: pricing ranges from $199 to $599 depending on the course and whether you choose instructor-led or self-paced learning. Pricing for corporate solutions is available on request.

Other recommendations from simplilearn: Machine learning certification training course , RPA with UIPath , Deep Learning Course

Certification: Yes

Bottom Line: Keep learning, Keep improving

AI will keep growing and improving, and so should you. In any career field, those that stay abreast of things with industry-relevant knowledge, always progress faster.

Even after you take an AI engineering course, don’t stop there. Keep learning, practice and keep improving. Live.edu has some good points about what you can do to keep getting better at AI.

  • Take part in AI projects, such as building chatbots. As your skills and confidence advance, take on new projects.
  • Share your projects with prospective employers and show them how you think your solutions will help their business.
  • Make your projects known publicly. You can code on public forums, or create and share a video where you talk about your projects and show off your code.
  • Keep reading. Industry experts have written a lot of AI books, and they are worth reading even after you get your dream job.

The great thing about working on forums is that you can get feedback from peers. You can use Open source platforms like GitHub to access learning opportunities and grow your skills.


Note: This is a guest post, and opinion in this article is of the guest writer. If you have any issues with any of the articles posted at www.marktechpost.com please contact at asif@marktechpost.com

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