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Series: Machine Learning PDF · 13 pages 0.2 MB · Updated 2026-06-26

phase0 math tooling

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PHASE 0 Math & Tooling Foundations Linear algebra, calculus, probability, statistics, and the scientific Python stack Phase goal Build the mathematical intuition that makes every later algorithm click instead of feeling

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Machine Learning PDF · 6p Updated 2026-06-26

00 overview

OVERVIEW Machine Learning & Deep Learning A depth-first curriculum, from math foundations to production Phase goal Turn “I can call .fit()” into “I understand, can implement, and can ship. ” This guide is the map for the

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Machine Learning PDF · 52p Updated 2026-06-20

1 1 Regression

ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2 ˆy = β⊤x R2REGRESSION FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from fitting a line through po

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Machine Learning PDF · 53p Updated 2026-06-20

1 2 Bayes

P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H) P (H| D) ∝ P (D|H ) P (H)BAYESIAN INFE

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Machine Learning PDF · 54p Updated 2026-06-26

1 3 Tree Based Methods

xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? TREE-BASED METHODS FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from a single decision tree through ran- dom forests and

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Machine Learning PDF · 54p Updated 2026-06-26

1 4 Trees

xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? xj < t ? TREE-BASED METHODS FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from a single decision tree through ran- dom forests and

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Machine Learning PDF · 53p Updated 2026-06-26

1 6 Support Vector Machines

w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b w⊤x + b SUPPORT VECTOR MACHINES FROM BEGINNER TO EXPERT A complete, visual, application-driven course — from the maximal-margin idea through soft margins, the kerne

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