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How to learn artificial intelligence (AI): the ultimate guide for beginners

Last updated: 09 February 2026

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Quick start: how to learn AI in 5 steps

  • Start with self-guided learning by exploring free tutorials and online resources on topics like machine learning, neural networks, and Python programming.

  • Consider studying an AI degree to gain a deep, structured understanding of AI and access to mentorship and research opportunities.

  • Focus on building key skills such as programming, mathematics, data science, and machine learning, as these are essential for working in AI.

  • Practice your knowledge by working on small AI projects or developing your own AI models to gain hands-on experience and improve your problem-solving abilities.

  • Explore entry-level AI jobs, such as Data Analyst or Junior Machine Learning Engineer, to start applying your skills and build your career.

Your guide to learning AI

Artificial intelligence (AI) has totally changed how we work and live, and it’s opening up lots of exciting new job opportunities. But for many people, AI can seem confusing or difficult to grasp.

This guide will explain what AI is and how to start learning it, especially if you’re thinking about studying it for a degree. We’ll walk you through the basics, the skills you’ll need, and the kinds of AI jobs hiring managers are looking to recruit for.

Female research scientist on tablet at night uses generative AI to help her analyze data.

What is artificial intelligence?

Artificial intelligence is when machines are designed to think and act like humans. These machines can do tasks that usually require human intelligence, like solving problems, learning from experience, making decisions, or even understanding language. AI can be very simple, like a chatbot that answers questions online, or very advanced, like self-driving cars that can make decisions while navigating on the road.

So, what does AI look like in everyday life? You’ve probably already used AI without even realising it. For example, when you get personalised suggestions for what to watch on Netflix or what music to listen to on Spotify, that’s AI at work. Smart home devices, like voice assistants (think Alexa or Google Home), also use AI to understand your requests and adapt to your habits. You might even have experimented with generative AI like ChatGPT.

AI allows machines to spot patterns in data, adjust to new information, and get better over time. This makes it useful for everything from helping doctors diagnose illnesses to creating safer cars.

Man in colourful check shirt and glasses using AI on a tablet to help his job as a marketing specialist.

How does artificial intelligence work?

At the heart of AI is a mix of data and algorithms. An algorithm is like a recipe – it’s a set of steps that tells a computer what to do with the data it’s given. AI collects huge amounts of data, processes it, and then uses these steps to figure out patterns and make decisions.

For example, think about facial recognition software. This works by taking a picture of a face and comparing it to a database of faces it's seen before. The algorithm will find patterns, like the distance between your eyes or the shape of your nose, to find a match.

AI learns from experience by using data to "train" the system. This means that the more training data and reinforcement learning it gets, the better it becomes at recognising patterns and making accurate predictions. AI can do this without needing a human to manually tell it what to do each time – this is where machine learning and deep learning come in. These are two important types of AI that help machines learn and get better on their own based on past data.

Difference between AI, data science, machine learning and deep learning

AI is a huge field, but it’s closely related to other areas like data science, machine learning, and deep learning. Even though people sometimes use these terms as if they mean the same thing, they’re actually different in important ways.

  • AI vs machine learning

    Machine learning is a part of AI. It’s all about teaching machines to learn from data by themselves, without a person needing to programme every little detail. The machine uses algorithms (like a set of instructions) to learn from patterns in the data. Over time, it gets better at tasks like sorting spam emails or recognising faces in photos, just by being exposed to more and more examples.

  • AI and data science

    Data science is all about working with huge amounts of unstructured data – gathering it, cleaning it, and analysing it to find actionable insights. AI often needs this processed data to work effectively. For example, in predictive analytics (a way of guessing what might happen based on past data), data science organises the raw data, and AI uses it to make accurate predictions. Essentially, data science and data analytics makes sense of the information, and AI makes decisions based on it.

  • Artificial intelligence and deep learning

    Deep learning takes machine learning techniques to the next level. It’s a more advanced form that tries to work like the human brain by using layers of "neural networks" – think of them like connected systems that help the machine process information in a smart way. Because of this, deep learning is able to handle really complex tasks, like translating languages in real-time or powering self-driving cars.

How to learn AI

Wondering how to start learning AI? There are a few different paths you can take depending on your time, resources, and goals. You might want to try a self-paced online course, work on AI projects, or dive deeper by getting a formal degree in AI.

Data scientist hoping to increase her average salary by learning AI.

Why should you study an AI degree?

Whether it’s a bachelor’s degree in AI or an advanced degree like an AI master’s degree, formal education in AI offers structured learning, industry expertise, and access to resources like mentorship, internship opportunities, and career guidance.


By studying AI at degree level, you’ll start with the basics, like programming languages and maths, building a strong foundation of knowledge and skills. As you progress in your degree, you'll use that foundation to learn about more complex areas like reinforcement learning, inference and causality, and natural language processing.

AI skills

Becoming an AI professional means building a variety of skills, from technical know-how to strong problem-solving abilities. Here are some of the key skills you’ll need to develop if you want to work in AI:

  • Statistics

    AI relies heavily on statistics to analyse data and find patterns. You'll need to understand probability and statistical methods to help AI systems make smart decisions and accurate results.

  • Mathematics

    Maths is crucial for AI, especially areas like linear algebra and calculus. These fields help AI systems process data and improve machine learning models, making it easier for systems to learn and adjust over time.

  • Programming

    Learning to code is essential for AI, and Python is one of the most popular languages thanks to its simplicity and powerful libraries. Writing code allows you to build, test, and improve your models.

  • Data science and management

    AI thrives on data, so understanding data science is key. You'll need to know how to collect more data (data mining), clean and manage large datasets, and extract knowledge from the data.

  • Computer science

    A strong understanding of key computer science concepts helps you understand how computers process and store information. Skills in algorithms and data structures are vital for solving AI problems and designing effective systems.

  • Machine learning

    Machine learning is simply teaching machines to learn from data. Understanding algorithms is key, as you'll need to know how to build models that allow machines to improve their performance based on experience.

  • Deep learning

    Deep learning uses neural networks to imitate the way the human brain processes information. This advanced AI skill helps tackle complex tasks, like image recognition and human language processing.

Artificial intelligence careers: What jobs can you have with an AI degree?

With an AI degree, you’ll have access to a wide range of AI roles. According to PwC UK's 2024 AI Jobs Barometer, jobs that need AI skills have grown 3.6 times faster than other jobs in the UK over the past decade.

Industries like transport, construction, and manufacturing are especially hiring AI professionals, with job openings growing 46% faster than in other sectors.

AI experts also earn more –about 14% higher than the average wage – while some jobs, like database designers, can pay up to 58% more than usual for people with AI skills.

Research scientist and data scientist use their AI skills to software engineer a virtual reality headset.

  • Here are some AI careers you could pursue, and the average salary* for each AI job:

    • Robotics engineer – £42,342:
      Robotics engineers design and build AI-powered robots. They create systems that allow robots to perform tasks autonomously or with minimal human assistance, using machine learning techniques and computer vision.
    • AI software developer – £44,611:
      AI software development involves creating applications that use AI to solve problems or automate tasks. Developers build tools like chatbots and search engines that use AI tech to improve efficiency and accuracy.
    • Big data engineer – £50,352:
      Big data engineers manage large data sets that AI systems rely on. They build platforms that allow AI to process and analyse data efficiently, ensuring models can access information for accurate predictions.
    • Deep learning engineer – £57,879:
      Deep learning engineers work with neural networks (systems that mimic human intelligence) to process large amounts of data. They focus on tasks like speech recognition and image analysis, building systems that can handle complex tasks with minimal human input.
    • Software engineer – £55,579:
      Software engineers in AI design and develop systems that run AI technologies. They write code for predictive models that manage data, make predictions, and support machine learning, ensuring smooth operation throughout the project.
    • Artificial intelligence engineer – £61,973:
      As an AI engineer, you'll build and deploy AI systems for tasks like natural language processing and computer vision. AI engineers integrate AI technologies into existing systems or create new AI-driven products to solve problems.
    • Machine learning engineer – £67,746:
      As a machine learning engineer, you'll develop systems that learn and improve over time. You'll design algorithms to help machines make decisions, working with large data sets to build models that improve with usage.

    *Salary information from glassdoor.co.uk and correct as of February 2026.

What are some entry-level jobs in AI?

There are several entry-level positions in AI that offer a strong foundation for your career.

You could work as a Data Analyst or Junior Data Scientist, preparing data for AI models. Junior Machine Learning Engineers assist in building and optimising machine learning models, while AI Software Developers work on coding AI-driven applications.

Even entry-level jobs like AI Support Engineers and Robotics Technicians allow you to troubleshoot AI applications or build AI-powered robots.

Two research scientists in glasses and shirts working using an AI to help their work.

The future of AI

The future of artificial intelligence is packed with potential. As AI technology continues to develop, we’ll see it being used in more advanced ways across different industries. But what does this really mean for us? Let’s take a closer look.

Will AI take over the world?

While AI is becoming a bigger part of our everyday lives, it’s important to understand that AI is still just a tool – one that humans have created to help with specific tasks. AI is great at doing things like recognising patterns and processing information quickly, especially in education, where it's helping to break down learning barriers and make knowledge more accessible to all.

However, AI still needs human oversight. It can’t make decisions entirely on its own or understand the world like humans do. So while AI will continue to grow and become more powerful, the idea of it “taking over the world” like in sci-fi movies is very unlikely.

Woman in shirt and glasses researching undergraduate degrees in AI and computer science.

What jobs could AI replace in the future?

AI is already automating many tasks, especially in industries like manufacturing (for predictive maintenance of machinery) and customer service. In these sectors, AI can handle repetitive jobs that don’t require a lot of human judgment, like data entry or routine analysis.

However, it’s important to remember that AI is also creating new jobs. As AI technology expands, we’re seeing an increasing demand for people skilled in AI development, machine learning, and data science. So while some roles might be automated, the job outlook is positive for people who can develop, manage, and improve AI systems.

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