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Data Science

Data Science allows us to extract meaning from and interpret data.

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Generative vs Discriminative Models: The Artist Who Paints vs The Critic Who Points
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Generative vs Discriminative Models: The Artist Who Paints vs The Critic Who Points

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9 min read
The Curse of Dimensionality: Why More Features Can Destroy Your Model Instead of Saving It
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The Curse of Dimensionality: Why More Features Can Destroy Your Model Instead of Saving It

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9 min read
L1 vs L2 Regularization: The Minimalist vs The Diplomat — Two Philosophies That Shape Your Model
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L1 vs L2 Regularization: The Minimalist vs The Diplomat — Two Philosophies That Shape Your Model

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10 min read
Regularization: The Art of Telling Your Model to Calm Down and Stop Overthinking
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Regularization: The Art of Telling Your Model to Calm Down and Stop Overthinking

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10 min read
Cross-Validation: Why Testing Your Model Once Is Like Judging a Restaurant by a Single Bite
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Cross-Validation: Why Testing Your Model Once Is Like Judging a Restaurant by a Single Bite

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11 min read
Learning Rate: The One Number That Can Make or Break Your Entire Model
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Learning Rate: The One Number That Can Make or Break Your Entire Model

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10 min read
Batch vs Mini-Batch vs Stochastic Gradient Descent: Three Hikers, Three Strategies, One Mountain
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Batch vs Mini-Batch vs Stochastic Gradient Descent: Three Hikers, Three Strategies, One Mountain

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10 min read
Gradient Descent: How to Find the Lowest Point in a Valley While Completely Blindfolded
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Gradient Descent: How to Find the Lowest Point in a Valley While Completely Blindfolded

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10 min read
Classification vs Regression: The Doctor Who Gives Answers vs The Doctor Who Gives Numbers
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Classification vs Regression: The Doctor Who Gives Answers vs The Doctor Who Gives Numbers

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9 min read
Loss Functions: The Brutally Honest Friend Your Model Desperately Needs
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Loss Functions: The Brutally Honest Friend Your Model Desperately Needs

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9 min read
Parametric vs Non-Parametric Models: The GPS vs The Taxi Driver
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Parametric vs Non-Parametric Models: The GPS vs The Taxi Driver

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9 min read
Overfitting & Underfitting: Why Your Model Aced the Practice Test But Failed the Real Exam
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Overfitting & Underfitting: Why Your Model Aced the Practice Test But Failed the Real Exam

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10 min read
The Bias-Variance Tradeoff: Why Your Model is Either Too Dumb or Too Smart
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The Bias-Variance Tradeoff: Why Your Model is Either Too Dumb or Too Smart

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7 min read
Chapter 2: Classification

Chapter 2: Classification

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4 min read
Data Science Meets Wine: Building a Model to Predict Quality Accurately
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Data Science Meets Wine: Building a Model to Predict Quality Accurately

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8 min read
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