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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.��Machine learning, however,��requires human intervention.

Although both deep learning and machine learning (ML) 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.

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 involves research, development and design of AI algorithms to improve upon existing artificial intelligence systems or create better models. Daily activities in ML might include any of the following:

• Data modeling
• Testing existing AI developments
• Developing algorithms to improve AI systems
• Collecting and sorting data for AI systems
• Documenting work

Those in ML also work with IT, data science and computer science. They’re often expected to work well as a team to improve AI systems.

Since machine learning handles data, it is actually considered a specialized field of data science. As such, IT skills such as data mining and modeling, statistical analysis and programming languages used in data science are also needed in��ML. These skills are also used in:

• Information systems (IS) management
• Data analysis
• Business intelligence analysis
• Database architecture
• Research science

The biggest difference between working in data science career and machine learning is that ML puts data into action��and alters ML systems based on this data.

According to the U.S. Bureau of Labor Statistics (BLS), computer and information research scientists who work with machine learning need at least a 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.

Machine learning requires a set of certain��hard skills. At minimum, these include:

• Knowledge of programming languages
• An understanding of technical subjects involved in machine learning
• Proficiency in advanced math
• An advanced understanding of AI and machine learning software

Soft skills for ML include:

• Attention to detail��— Even the smallest amounts of code or data can affect ML software.��
• Analytical skills��— ML means analyzing and organizing data to develop AI programs.��
• Problem-solving��— Knowing how to problem-solve will help address any kinks in ML and AI systems along the way.
• Teamwork and communication��— Since ML works with data science and IT, it’s important to know how to communicate and work with others.

If you’re interested in learning more about technologies like machine learning, ��ɫ��Ƶ offers several online IT programs, including those in data science, cybersecurity, and computer science.