Artificial intelligence can feel like one of those topics everyone talks about but few people fully understand. Terms like ‘machine learning,’ ‘deep learning,’ and ‘neural networks’ are everywhere. It can seem tricky to learn what they mean in a more technical sense, but understanding them is important if you’re interested in getting into software development or data engineering.
So what actually is a neural network? And how does it relate to data engineering?
Neural networks, explained
A neural network is a computer system designed to recognise patterns and make decisions using data. It is inspired by the human brain, although it works in a much simpler way.
Think about how humans learn. If you show a child enough pictures of cats, eventually they start recognising what a cat looks like. They unconsciously notice patterns like whiskers, ears, fur, eyes and shape, which help differentiate cats from other animals.
A neural network learns in a similar way. Instead of being directly programmed with every rule, it learns from examples. For instance, if you feed a neural network thousands of photos labelled “cat” and “not cat”, it gradually learns which features matter most. Over time, it becomes better at spotting cats it has never seen before.
Why is it called a ‘neural’ network?
The name comes from biology. Your brain contains billions of neurons connected together which send signals to help you think, move and understand information. Artificial neural networks copy this idea using layers of connected nodes. Each node receives information, processes it, and passes it forward.
A typical neural network has:
- An input layer: where data enters the system
- Hidden layers: where the learning happens
- An output layer: where the final prediction or answer appears
Here’s a simple example:
- Input: a photo
- Hidden layers: analyse shapes, colours and patterns
- Output: ‘This is a cat’
The more data the network sees, the more accurate it can become.
How do neural networks learn?
Neural networks learn through repetition. At first, the system makes guesses that are often wrong. It then compares its answer with the correct answer and adjusts itself slightly.
This process repeats thousands or even millions of times, and eventually, the network becomes much better at making predictions. This training process is one of the foundations of modern machine learning and AI systems.
Where are neural networks used?
Neural networks power many of the tools people use every day.
Some common examples include:
- Voice assistants like Siri and Alexa
- Recommendation systems on Netflix and Spotify
- Fraud detection in banking
- Image recognition in healthcare
- Translation tools
- Generative AI platforms like ChatGPT
They are especially useful when dealing with huge amounts of complex data.
Why neural networks matter in data engineering
Neural networks don’t work in isolation. Before an AI model can learn, data needs to be collected, cleaned, stored and processed properly. That is where data engineering becomes essential.
A strong AI system relies on key elements of a data engineer’s role, such as:
- High-quality datasets
- Reliable data pipelines
- Cloud infrastructure
- Scalable storage systems
- Monitoring and optimisation
In real-world companies, data engineers and machine learning engineers often work closely together. That is why modern AI careers increasingly require a combination of programming, data handling and machine learning skills.
The future of AI careers
AI is no longer limited to large tech companies. As businesses across finance, healthcare, retail, logistics and media invest heavily in machine learning and data systems, there is growing demand for people who understand data engineering and AI infrastructure, including neural networks.
A common misconception about AI is that you need a mathematics degree or years of coding experience to get started. In reality, many people begin with the basics: learning Python, understanding how data works, building simple machine learning models, and experimenting with neural networks using real datasets.
Northcoders’ Data Engineering, AI & Machine Learning bootcamp is designed to teach learners these foundations in a structured way. The bootcamp covers Python, SQL, cloud engineering, data pipelines and AI concepts including neural networks, LLMs, embeddings and fine-tuning models.
To sum up, neural networks are at their heart systems designed to learn patterns from data. They are one of the key technologies behind modern AI, powering everything from recommendation engines to generative AI tools. If you’re interested in building practical AI skills, Northcoders’ Data Engineering, AI & Machine Learning bootcamp provides practical learning to take your career into the world of data and AI. You can learn more and get started here.