7 AI Skills to Learn for Your Career

AI is everywhere. It’s automating tedious and manual processes across thousands of jobs so employees can focus on the tasks that make their companies stronger. These are the AI skills to learn that will help you keep growing in your career.

Why You Need to Learn How to Use AI for Work

Practically every job in the American landscape uses some form of AI, no matter the industry. This technology goes beyond the generative tools you might recognize from ChatGPT—it also includes programs that detect potential bugs in new software code and the technology that allows farmers to plant and harvest crops efficiently so they can feed more people and livestock.

Having the skills to develop and use AI effectively helps you do your best work and understand how you did it. Gaining those skills doesn’t have to involve a four-year degree or doctorate program, though—you can gain many of them through a few months of specialized training.

Where to Learn Professional AI Skills

There are hundreds of free or low-cost tools you can use to master AI skills online. SkillUp works with some of the top educators in software development to help people like you learn the ropes. Here are some of the most popular programs that let you hone your AI development skills:

• Full-stack software development
• Web development
• Programming languages
• Systems administration

The Highest-Demand AI Skills to Have

1. AI Ethics and Bias Mitigation

AI utilization has become mainstream enough that it’s occasionally being used as a way to automate tasks. While this can help employees work faster, they might also accidentally expose sensitive data or plagiarize information for things like written content.

That’s why more and more companies are looking for candidates who have experience with AI ethics skills. These experts help organizations ensure their data is secure and meets the quality standards that distinguish them from competitors who are publishing AI-generated content without a thorough review.

Bias mitigation is an equally important piece of the puzzle, especially since AI learns from data and information with inherent biases. Companies rely on trained professionals to recognize and address these biases so the AI in their applications is designed with accountability and transparency.

For example, if someone is developing a program that helps HR specialists with the hiring process, a bias mitigation expert can help identify and eliminate biases based on an applicant’s name, age, or sex.

You don’t even necessarily need to be a developer to have and use this skill set. While it requires training like any other type of skill, it doesn’t necessarily require advanced coding knowledge and relies more upon analytical skills.

2. Machine Learning

Machine learning involves teaching computers how to learn from the data you provide them. Doing so helps them improve their accuracy without needing explicit instructions.

A prime example of this comes straight from your email provider. Machine learning helps the algorithms powering your inbox recognize common keywords, suspicious addresses, and other indicators that an email is suspicious or possibly spam.
However, machines won’t learn how to recognize these markers on their own and occasionally need to be updated. Revisiting our example, spammers can always find more sophisticated ways to send emails. That’s where people with machine learning experience come in.

By training machines to recognize the latest types of spam, the contents of images, or even medical diagnoses, you become part of the solution that makes technology easier to use. This specialty will continue to grow with the market over time.

3. Natural Language Processing

Natural language processing is a technological tool that helps computers understand and interpret human language. It combines human language modeling with statistical and machine learning models to make it easier to give computers voice recognition and command capabilities.

This technology makes it easier for people to communicate with each other using things like translation apps for one-to-one conversations.

Learning how to code can help you sharpen this technology and make AI systems better at serving humans where they most need them.

4. Computer Vision

The same technology that makes reverse image searches possible can also keep motorists safe while driving or detect cancerous masses and other anomalies during CT scans or MRIs. Computer vision helps systems extract information from digital images or videos.

Honing the computer vision skills for any piece of technology requires going a step beyond standard development techniques. You’ll need to take courses that help you learn about image processing techniques, as well as specialized open-source libraries and proprietary software focused on images.

5. Reinforcement Learning

Commonly used in robotics, video games, and navigation systems, this form of machine learning helps software maximize cumulative rewards or adjustments. It’s essential to use environmental simulation programs to get familiar with reinforcement learning and how it works if you’re exploring tech careers.

6. Data Science & Analytics

Being able to extract knowledge from the data AI produces or gathers is essential for helping businesses make informed decisions. Data science and analytics are at the core of this practice. They also help predict consumer trends and user behavior.

Having data science and analytics skills can make you part of the equation that helps companies grow and change lives.
If you have strong statistical skills and are hungry to learn more about coding languages like Python or R, you may find having big data analytics skills to be a useful opportunity. A great way to hone your data analytics skills is to learn about data visualization and manipulation tools like SQL.

7. Robotics

The robotics industry is always looking for new engineers and developers who can help machines learn tasks that normally require human intelligence. Whether you want to design machines that assemble basic parts on cars or handle dangerous materials like toxic or radioactive substances, robotics skills are always in high demand.

Learning how to code is essential in robotics and can help you develop a long, far-reaching career. It’s also helpful to be familiar with specialized coding languages so you can help robots function more effectively.