6 In-Demand AI Skills & How to Learn Them

5 minutes

Hiring tech pros with AI skills is more difficult than any other area of tech. Simply put, the growing demand for AI experts outweighs the supply — organizations are eager to incorporate AI into their businesses, but there’s a limited pool of AI talent to choose from. In fact, in Skillsoft’s IT Skills and Salary Report 2023, 30% of IT decision-makers report having the most difficulty hiring qualified AI professionals.

So, what gives? Tech teams and job seekers are certainly up for the challenge of learning new skills. But keeping pace with rapidly changing AI technology and discerning which skills are worth investing in can be difficult for individuals. The good news: Learning in-demand AI skills can boost your earning potential and help you stand out in the job market.

Learning about AI with Codecademy is one of the smartest things you can do to further your career in tech. Our courses are interactive and utilize industry-leading AI tools, so you can get hands-on experience working with AI systems as you learn about the concepts that power them. And with our career paths, you can build a portfolio of impressive projects that show employers you can apply AI skills to real-world scenarios.

If you’re not sure where to begin, here are the most in-demand AI concepts, programming languages, frameworks, and systems as well as the courses and paths to start learning these skills.

Generative AI  

A report from Indeed examined which tech skills can have the biggest impact on salaries. When a job included “generative AI” as a desired skill, the salary rose 47%, making it the highest-paying skill in the report. While it doesn’t take any technical knowledge to write or say a prompt for ChatGPT, knowing the tactical ways to deploy generative AI in your coding work will expedite and level up your development work. Get under the hood and gain experience building complex AI models. Who knows? You could be the one to drive the future of software innovation. 

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Machine learning  

Machine learning is the process of using algorithms to detect patterns in data and build models that represent aspects of reality. These models (whether accurate or not) are trained to perform various tasks. Machine Learning Engineers develop systems that enable computers to learn from data and make predictions, which involves tasks like creating scalable code, maintaining data pipelines, and building real-time applications. They collaborate with Data Scientists to implement and scale models, and their work requires a blend of programming skills and problem-solving abilities. 

Understanding machine learning opens new opportunities to build innovative solutions that enhance efficiency and provide valuable insights across industries. For example, e-commerce platforms use personalized recommendation systems to guide customers to products. In cloud computing, machine learning helps optimize resource allocation. And in healthcare, machine learning algorithms analyze medical images and aid in diagnosing and treating diseases. 

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Deep learning 

Deep learning is a subset of machine learning that uses neural networks that are trained on datasets to make predictions based on previously encountered scenarios. According to the Indeed report, people who understand deep learning make 47% more than those who don’t. If you know Python, NumPy, and some machine learning basics, you can create neural networks using frameworks like TensorFlow. PyTorch is another free open-source framework that offers an optimized tensor library for deep learning. PyTorch is developed by Meta and can be used for natural language processing and computer vision apps. 

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AI for coding processes 

With generative AI tools, developers can refine and optimize their workflows. When GitHub surveyed people who use GitHub Copilot, they found that developers code faster, feel less frustrated, and since they’re not bogged down by repetitive tasks, they have more mental energy to focus on fulfilling work.  

We recently added an AI Learning Assistant to select courses, so you can get instant personalized coding guidance and chat through your questions without leaving the platform. This tool is an excellent way to get practice working through coding problems with a generative AI sidekick and improve your prompting skills.   

There are countless ways that engineers use tools like GitHub Copilot and ChatGPT to be more efficient, thorough, and creative. And as AI models get more advanced, you can discover even more techniques for utilizing generative AI in your coding work.  

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Popular AI systems 

Just like you know when and how to use various programming languages, you should also understand the unique functionalities and use cases of all the industry-leading AI platforms. OpenAI, for example, focuses on developing and deploying general-purpose AI models through APIs that programmers can integrate into their own applications. Hugging Face is a community-driven platform that provides tools and resources for NLP and machine learning. And Midjourney specializes in AI-powered image generation and operates through Discord.  

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Unlock your potential with AI skills 

Check out our full catalog to find the AI courses and paths that fit your interests and goals as a developer. Want to work towards a career in AI? Learn more about the lucrative and rewarding careers you can have in AI, or read this list of standout AI skills for freelancers to have.  

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