If you are exploring machine learning, you will quickly notice one subject coming up everywhere: linear algebra. Don’t worry, you don’t need to be a mathematician. With a few basics—vectors, matrices, and some operations—you can start understanding how machine learning models really work.
What is Linear Algebra?
Linear algebra is the study of vectors (lists of numbers) and matrices (grids of numbers). Instead of solving single equations, you can handle entire datasets.
A vector like [2, 3] can be seen as a point in 2D space.
A matrix like:
[1 2 3]
[4 5 6]
is just numbers arranged in rows and columns.
Why Does ML Need Linear Algebra?
Data Representation: Datasets are stored as matrices.
Transformations: Scaling, rotation, and projections are matrix multiplications.
Optimization: Training models like regression or neural networks relies on solving equations using linear algebra.
The Basics You Actually Need
To start with machine learning, focus on:
Vectors and matrices
Multiplication, transpose, inverse
Dot products
Eigenvalues and eigenvectors
Where It Shows Up in ML
Computer Vision: Images are pixel matrices.
NLP: Words and sentences are converted into vectors.
Recommendation Systems: Predicting user interests uses matrix factorization.
Deep Learning: Every hidden layer is powered by matrix multiplication.
Final Note
Linear algebra is the backbone of machine learning. By learning just the basics, you’ll find it much easier to follow algorithms, build small projects, and grow deeper step by step.
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