By University of Phoenix
If you’ve heard of artificial intelligence (AI), you’ve most likely run into different subsets of the technology, including machine learning and deep learning. At its core, AI attempts to mimic human behavior but can take many forms, such as chatbots or self-driving cars.
Although both deep learning and machine learning work within the same theoretical family as AI, there are notable differences. For one, deep learning relies more on data sets and creating predictions of these data sets on their own — all without human intervention.
Machine learning, however, requires human intervention, and that’s where machine learning engineers enter the picture.
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AI is an innovative field that continues to grow. As such, employers will likely be seeking individuals who have knowledge in this technical field, including machine learning.
Machine learning engineers* research, develop and design AI algorithms to improve upon existing artificial intelligence systems or create better models. Daily responsibilities might include any of the following:
In their daily roles, machine learning engineers also work with other IT team members, data scientists and computer science specialists. They’re often expected to work well as a team to improve AI systems.
*University of Phoenix does not educationally prepare students to become a machine learning engineer. However, there are other information technology programs to consider if the world of technology interests you.
Since machine learning engineers handle data, machine learning is actually considered a specialized field of data science. As such, learning about data science can help prepare you with IT skills for work in machine learning. You’ll learn about data mining and modeling, statistical analysis and programming languages — all of which can be required of a machine learning engineer.
Alternatively, you may jump into other data science jobs, such as:
The biggest difference between working in a data science career and a machine learning engineer career is that machine learning engineers put data into action and alter machine learning systems based on this data.
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A blend of education, skills and experience is necessary to become a machine learning engineer. Here’s one path you can take:
According to the U.S. Bureau of Labor Statistics (BLS), computer and information research scientists who work with machine learning need at least a master’s degree in computer science or a related field. This can include a Master of Science in Computer Science or, if you’re looking to become a data scientist, a Master of Science in Data Science, since machine learning is a subset of the field.
To meet the standards of most machine learning roles, you’ll need a set of certain hard and soft skills. At minimum, you’ll need:
Hiring managers will also look at your personality, which should be supported by soft skills, such as:
Once you have a relevant degree and skills, you can begin to apply for entry-level positions. When doing this, it’s important to find ways to stand out from your competitors. One such way is to build up your experience with machine learning so you can list it within your resumé. This can be anything from shadowing experience with other machine learning engineers to an internship.
Although this experience may not guarantee you a position, it will highlight your knowledge of a machine learning working environment and any skills you developed during that time.
Machine learning is a field that will continue to grow as long as technology continues to develop. It will require engineers who are open to continual learning throughout their career. Being willing to adapt, grow and learn are important aspects to working in the field of technology.
While University of Phoenix does not educationally prepare students to become machine learning engineers, there are several information technology degrees to consider if IT or data science interests you.
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