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Linear Algebra for ML
in 5 Days

Vectors, matrices, eigenvalues, PCA, and their direct applications in machine learning — from neural networks to dimensionality reduction.

5 lessons
Self-paced
~60 min/day
Free forever
No account needed
Start Day 1 — Free → See what's covered
What you'll learn

Linear Algebra for ML in 5 Days

Free 5-day linear algebra for machine learning course. Vectors, matrices, eigenvalues, PCA, and AI applications.

  • Perform vector operations and understand geometric intuition
  • Multiply matrices and compute inverses and transposes
  • Calculate determinants and understand their meaning
  • Find eigenvalues and eigenvectors step by step
  • Implement PCA for dimensionality reduction
  • See how linear algebra powers neural networks and SVD
Eigen
decomposition
PCA
& SVD
5
lessons
$0
cost
5-Day Curriculum

Every lesson has a real exercise.
No theory without practice.

01
Vectors & Geometry
Vector addition, scaling, dot product, cross product, norms, projections
dot productnormsprojections
→
02
Matrices & Operations
Matrix multiply, transpose, inverse, identity, rank, null space
matrix multiplyinverserank
→
03
Determinants & Systems
Determinant calculation, Cramer's rule, solving Ax=b, LU decomposition
determinantLU decompositionAx=b
→
04
Eigenvalues & Eigenvectors
Characteristic polynomial, diagonalization, spectral theorem, power iteration
eigenvaluesdiagonalizationpower iteration
→
05
PCA & ML Applications
SVD, PCA algorithm, variance explained, neural network weight matrices
PCASVDneural networks
→
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Your instructor
B
Bo Peng
Founder, Precision AI Academy

Kaggle Top 200 data scientist. Trained 400+ professionals across universities and federal agencies. Built production systems at scale. This course is practical first — every lesson has working examples and an exercise you can finish in under an hour.

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