chore: import upstream snapshot with attribution

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+Choosing a classification algorithm
+First steps with scikit-learn
+    Training a perceptron via scikit-learn
+Modeling class probabilities via logistic regression
+    Logistic regression intuition and conditional probabilities
+    Learning the weights of the logistic cost function
+    Training a logistic regression model with scikit-learn
+    Tackling overfitting via regularization
+Maximum margin classification with support vector machines
+    Maximum margin intuition
+    Dealing with the nonlinearly separable case using slack variables
+    Alternative implementations in scikit-learn
+Solving nonlinear problems using a kernel SVM
+    Using the kernel trick to find separating hyperplanes in higher dimensional space
+Decision tree learning
+    Maximizing information gain – getting the most bang for the buck
+    Building a decision tree
+    Combining weak to strong learners via random forests
+K-nearest neighbors – a lazy learning algorithm
+Summary