Machine Learning
Core machine learning concepts from regression and classification to overfitting, regularization, hyperparameter tuning, and ensemble methods.
15 posts
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Deep LearningLoss vs Accuracy
The distinction between loss (cross-entropy) and accuracy in neural network training, why they can diverge and what each metric tells you.
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Data ScienceLoss functions
A survey of common loss functions MSE, cross-entropy, hinge loss, and when to use each for regression and classification problems.
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Data ScienceMethods of Hyperparameter optimization
Comparing hyperparameter optimization strategies like grid search, random search, and Bayesian optimization with scikit-learn examples.
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MathematicsA visual introduction to eigenvectors and eigenvalues
A geometric, visual explanation of eigenvectors and eigenvalues through linear transformations such as scaling, rotation, and shearing.
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Data ScienceScaling vs Normalization
The difference between feature scaling (min-max) and normalization (standardization), and when to apply each in machine learning pipelines.
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Data ScienceEnsembling is the key
An overview of ensemble learning methods: bagging, random forest, boosting, and stacking, and why combining models often outperforms any single algorithm.
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Data ScienceGradient boosted trees: Better than random forest?
Comparing gradient boosted trees and random forests, their differences in training strategy, tuning requirements, and when to prefer each.
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Data ScienceData Mining: Knowledge discovery in databases
An overview of the KDD (Knowledge Discovery in Databases) process and how data mining, machine learning, and data science relate to each other.
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Data Science
The Curse of Dimensionality
Why increasing the number of features degrades kNN performance, the curse of dimensionality explained intuitively and mathematically.
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Data Science
Regularization
How regularization techniques, L1 (Lasso) and L2 (Ridge), add penalty terms to the loss function to combat overfitting in linear models.
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Data ScienceSimplicity doesn't imply accuracy
Examining Occam's razor in machine learning, why simpler models aren't always more accurate and how complexity relates to overfitting.
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Data ScienceOverfitting and Underfitting
Explaining overfitting and underfitting in machine learning, and how the bias-variance tradeoff helps build better-generalizing models.
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Data ScienceEmail spam filtering: Text analysis in R
Building and evaluating an email spam filter using text analytics and machine learning in R.
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Data ScienceMoneyball: Why no prediction can't be made for baseball champion
Using logistic regression in R to explore why ML cannot reliably predict the baseball World Series champion.
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Data ScienceMoneyball: How linear regression changed baseball
How Oakland A's used linear regression in R to identify undervalued players and compete despite limited budget.