Which Maths Do You Need for ML?
Build a realistic maths study plan matched to the kind of AI work you want to do
A taste of a lesson
I want to build ML models at work. Should I finish a full linear algebra course before starting machine learning?
No need to finish it first. For building classical models, you need vectors, matrices, dot products and matrix multiplication, which a few weeks of focused study covers. Learn those, then start a simple regression project, and pick up the next topics, such as eigenvectors for PCA, when a project calls for them. Maths learned just before you use it sticks far better. Let me check your starting point: can you compute the dot product of (1, 2, 3) and (2, 0, 1)?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Clarify an AI learning goal and map it to the maths it needs
- Assess your current maths level with a short diagnostic
- Build a week by week study plan with practice and checkpoints
- Pair maths topics with small coding or data exercises
- Adjust the plan as you progress without losing momentum
Lesson plan
- 1 Your goal and your time Define what you want to do with AI and the time you have. Start
- 2 Where you are now Find your starting point with friendly diagnostic questions. Start
- 3 Mapping maths to goals List the maths topics your goal actually requires. Start
- 4 Building the plan Turn topics into a realistic week by week schedule. Start
- 5 Study habits that work Use practice methods that make maths stick. Start
- 6 Reviewing and adjusting Check progress honestly and adapt the plan. Start
Try asking
About this tutor
A planning tutor for anyone at any level who wants to learn maths for machine learning without getting lost. You will clarify your goal, whether using AI tools well, building models, engineering ML systems or reading research, and map it to the statistics, probability, linear algebra, calculus and optimisation you actually need. The tutor checks your current level with a few friendly questions, builds a week by week plan with practice and checkpoints, and suggests how to combine maths with small coding projects. You can return to adjust the plan as you progress.
Reviews
4.3
3 ratingsSample
- Fiona C.Sample
Realistic about my limited hours. The diagnostic questions were quick and pinpointed my gaps.
- Abdul R.Sample
Helpful planning sessions. I come back monthly to adjust the plan, which keeps me honest.
- Nikhil J.Sample
I had been stuck in maths prerequisites for a year. The just ahead of need plan got me building models within weeks.
About the teacher
Calculus and optimisation for machine learning, done by hand
9 tutors 319 lessons taught Sample
I teach the calculus and optimisation that make models learn: derivatives, gradients, the chain rule, gradient descent and the loss functions it minimises. My background is in engineering and numerical computing, so I care about why things work and also about when they break, such as unstable learning rates or overflowing exponentials. I teach with pencil calculations first, then a...
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