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Machine Learning

Implement the math behind models, metrics, and training.

4 stops · 26 problems0/26 on the path done
next ▸ Classification Accuracy ScoreSilver · 1150
1

Metrics & losses

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Accuracy, precision/recall, errors, entropy.

○Classification Accuracy Score○Mean Squared Error○Mean Absolute Error○Binary Confusion Matrix Counts○Root Mean Squared Error○Binary Precision Score○Binary Recall Score○Binary F1 Score○Gini Impurity○R Squared Score○Label Distribution Entropy○Binary Cross Entropy Loss○Information Gain Of A Split
2

Vectors & similarity

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Dot product, cosine, distances, normalize.

○Vector Dot Product○Euclidean Distance○Min Max Normalize○L2 Unit Normalize○Z Score Standardize○Cosine Similarity
3

Activations & models

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Sigmoid, ReLU, softmax, regression, kNN, k-means.

○Sigmoid Of A Scalar○ReLU Over A Vector○KNN Majority Vote○Softmax Of A Vector○K Means Assignment Step○Pearson Correlation○Gradient Descent Step 1D
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More ML

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Everything else in the ML toolbox.

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