If you're stepping into a role in AI or machine learning and feel nervous about the maths side of things, you're not alone. Many people entering the tech world from other fields worry they’ll have to become maths wizards overnight. Here’s the good news: you don’t. But a basic understanding of a few key concepts will go a long way in helping you feel confident and capable.
This article breaks down the essential maths you need for machine learning, how it’s used in real-world projects, and where to learn it — no maths degree required.
Why maths matters in machine learning (and why it’s not as scary as it sounds)
Machine learning models rely on numbers to find patterns in data and make decisions. That means some maths is involved, but you won’t be solving textbook equations from memory.
Instead, maths helps you:
- Understand how algorithms work under the hood
- Choose the right model for your data
- Spot errors or weird behaviour in a system
- Communicate ideas clearly to teammates or stakeholders
Think of it like learning basic car maintenance. You don’t need to be a mechanic to drive, but it helps to know what an engine is and how to change a tyre.
The big three: key maths topics for ML
Here are the three most important areas of maths for machine learning — explained in plain English.
1. Linear algebra (vectors and matrices)
What it is: Linear algebra is the maths of rows and columns of numbers. A vector is a list of numbers. A matrix is a table of numbers. They’re used to represent data or operations in machine learning.
How it shows up in ML:
- Image data is often stored as matrices
- Text models convert words into vectors (called embeddings)
- Most model training involves matrix operations under the hood
How to learn it:
2. Probability and statistics
What it is: Probability is about making predictions and measuring uncertainty. Statistics helps us summarise and understand data.
How it shows up in ML:
- Classifiers predict the likelihood of something being true (e.g. spam or not)
- Models need to account for randomness and noise in data
- Evaluating models often involves statistical measures like accuracy or precision
How to learn it:
- YouTube: Statistics Fundamentals by StatQuest with Josh Starmer [60 videos]
- EdX: Data Science: Probability by HarvardX
3. Basic calculus
What it is: Calculus is about change — especially how things increase or decrease over time. You may remember derivatives from school; they describe how fast something is changing.
How it shows up in ML:
- Training models involves something called “gradient descent”, which uses derivatives to improve performance step by step
- Loss functions (used to measure model accuracy) rely on calculus to minimise error
How to learn it:
Real-world examples
Here’s how these maths concepts are applied in the real world:
- Email spam filters use probability to decide if a message is junk or not.
- Self-driving cars use vectors and matrices to understand objects in space.
- Recommendation systems (like on Netflix or Spotify) use matrix operations to find similarities between users and content.
- Chatbots and language models rely heavily on vector representations of words.
You don’t need to write the maths behind these systems from scratch — but recognising what’s happening and why gives you a serious edge.
How to build maths confidence (without going back to uni)
Here are a few ways to get comfortable with maths at your own pace:
Start small. Choose one topic and watch a short video each day. Don’t rush it.
Use visual explainers. They’re often easier to follow than abstract formulas.
Apply what you learn. Try simple coding exercises that use vectors or probability.
Ask questions early. Whether you’re on the job or in a course, there’s no shame in asking for clarification.
Find a study buddy or mentor. Talking it out helps reinforce your learning.
You’re closer than you think
You don’t need to love maths to succeed in AI — but learning just enough can make you feel more capable and less intimidated.
Focus on understanding the concepts, not memorising formulas. Use the right tools to learn in a way that works for you. And remember: every expert once started where you are now.
Whether you’re learning to fine-tune a model or just trying to follow a conversation at work, a little maths knowledge goes a long way.
Keep it steady. Keep it practical. You’ve got this.