Foundations
Python, NumPy, linear algebra and probability — the toolkit under ML
25 cards · first 3 free · flip each card to test yourself
04
Classes, Objects & Dunder Methods
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05
Vectorization
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06
Broadcasting
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07
Views vs Copies
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08
Axis Reductions
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09
Pandas GroupBy & Missing Data
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10
Dot Product & Projection
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11
Matrices as Linear Maps
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12
Rank, Column Space & Null Space
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13
Eigenvectors & Eigenvalues
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14
SVD & PCA
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15
Least Squares & the Normal Equations
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16
Derivatives & the Chain Rule
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17
Gradients, Jacobians & Hessians
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18
Random Variables & Expectation
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19
Conditional Probability & Bayes' Theorem
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20
Common Distributions
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21
Covariance & Correlation
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22
Law of Large Numbers & the CLT
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23
Maximum Likelihood Estimation
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24
Hypothesis Testing & p-values
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25
Entropy, Cross-Entropy & KL
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