chore: import upstream snapshot with attribution

code/_convenience_scripts/blank_tocs/ch01.toc

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+Building intelligent machines to transform data into knowledge
+The three different types of machine learning
+  Making predictions about the future with supervised learning
+    Classification for predicting class labels
+    Regression for predicting continuous outcomes
+  Solving interactive problems with reinforcement learning
+  Discovering hidden structures with unsupervised learning
+    Finding subgroups with clustering
+    Dimensionality reduction for data compression
+An introduction to the basic terminology and notations
+A roadmap for building machine learning systems
+  Preprocessing – getting data into shape
+  Training and selecting a predictive model
+  Evaluating models and predicting unseen data instances
+Using Python for machine learning
+  Installing Python packages
+Summary

code/_convenience_scripts/blank_tocs/ch02.toc

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+Artificial neurons - a brief glimpse into the early history
+of machine learning
+Implementing a perceptron learning algorithm in Python
+  Training a perceptron model on the Iris dataset
+Adaptive linear neurons and the convergence of learning
+  Minimizing cost functions with gradient descent
+  Implementing an Adaptive Linear Neuron in Python
+  Large scale machine learning and stochastic gradient descent
+Summary

code/_convenience_scripts/blank_tocs/ch03.toc

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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

code/_convenience_scripts/blank_tocs/ch04.toc

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+Dealing with missing data
+  Eliminating samples or features with missing values
+  Imputing missing values
+  Understanding the scikit-learn estimator API
+Handling categorical data
+  Mapping ordinal features
+  Encoding class labels
+  Performing one-hot encoding on nominal features
+Partitioning a dataset in training and test sets
+Bringing features onto the same scale
+Selecting meaningful features
+  Sparse solutions with L1 regularization
+  Sequential feature selection algorithms
+Assessing feature importance with random forests
+Summary

code/_convenience_scripts/blank_tocs/ch05.toc

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+Unsupervised dimensionality reduction via principal component analysis 128
+  Total and explained variance
+  Feature transformation
+  Principal component analysis in scikit-learn
+Supervised data compression via linear discriminant analysis
+  Computing the scatter matrices
+  Selecting linear discriminants for the new feature subspace
+  Projecting samples onto the new feature space
+  LDA via scikit-learn
+Using kernel principal component analysis for nonlinear mappings
+  Kernel functions and the kernel trick
+  Implementing a kernel principal component analysis in Python
+    Example 1 – separating half-moon shapes
+    Example 2 – separating concentric circles
+  Projecting new data points
+  Kernel principal component analysis in scikit-learn
+Summary

code/_convenience_scripts/blank_tocs/ch06.toc

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+Streamlining workflows with pipelines
+  Loading the Breast Cancer Wisconsin dataset
+  Combining transformers and estimators in a pipeline
+Using k-fold cross-validation to assess model performance

code/_convenience_scripts/blank_tocs/ch07.toc

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+Learning with ensembles
+Implementing a simple majority vote classifier
+  Combining different algorithms for classification with majority vote
+Evaluating and tuning the ensemble classifier
+Bagging – building an ensemble of classifiers from bootstrap samples
+Leveraging weak learners via adaptive boosting

code/_convenience_scripts/blank_tocs/ch08.toc

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+Obtaining the IMDb movie review dataset
+Introducing the bag-of-words model
+  Transforming words into feature vectors
+  Assessing word relevancy via term frequency-inverse document frequency 
+  Cleaning text data
+  Processing documents into tokens
+Training a logistic regression model for document classification

code/_convenience_scripts/blank_tocs/ch09.toc

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+Serializing fitted scikit-learn estimators
+Setting up a SQLite database for data storage 
+Developing a web application with Flask
+Our first Flask web application
+  Form validation and rendering
+  Turning the movie classifier into a web application

code/_convenience_scripts/blank_tocs/ch10.toc

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+Introducing a simple linear regression model
+Exploring the Housing Dataset
+  Visualizing the important characteristics of a dataset
+Implementing an ordinary least squares linear regression model
+  Solving regression for regression parameters with gradient descent
+  Estimating the coefficient of a regression model via scikit-learn
+Fitting a robust regression model using RANSAC
+Evaluating the performance of linear regression models
+Using regularized methods for regression
+Turning a linear regression model into a curve – polynomial regression
+  Modeling nonlinear relationships in the Housing Dataset
+  Dealing with nonlinear relationships using random forests

code/_convenience_scripts/blank_tocs/ch11.toc

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+Grouping objects by similarity using k-means
+  K-means++
+  Hard versus soft clustering
+  Using the elbow method to find the optimal number of clusters
+  Quantifying the quality of clustering via silhouette plots
+Organizing clusters as a hierarchical tree
+  Performing hierarchical clustering on a distance matrix
+  Attaching dendrograms to a heat map
+  Applying agglomerative clustering via scikit-learn
+Locating regions of high density via DBSCAN
+Summary

code/_convenience_scripts/blank_tocs/ch12.toc

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+Modeling complex functions with artificial neural networks
+  Single-layer neural network recap
+  Introducing the multi-layer neural network architecture
+  Activating a neural network via forward propagation
+Classifying handwritten digits
+  Obtaining the MNIST dataset
+  Implementing a multi-layer perceptron
+Training an artificial neural network
+  Computing the logistic cost function

code/_convenience_scripts/blank_tocs/ch13.toc

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+Building, compiling, and running expressions with Theano
+  What is Theano?
+  First steps with Theano
+  Configuring Theano
+  Working with array structures
+  Wrapping things up – a linear regression example
+Choosing activation functions for feedforward neural networks
+  Logistic function recap
+  Estimating probabilities in multi-class classification via the softmax function

code/_convenience_scripts/ipynb_toc_links.py

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+# Sebastian Raschka, 2015
+# convenience function for myself to add internal links to IPython toc
+# use as `python ipynb_toc_links.py /blank_tocs/ch01.toc`
+
+import sys
+
+ipynb = sys.argv[1]
+with open(ipynb, 'r') as f:
+    for line in f:
+        out_str = ' ' * (len(line) - len(line.lstrip()))
+        line = line.strip()
+        out_str += '- [%s' % line
+        out_str += '](#%s)' % line.replace(' ', '-')
+        print(out_str)

code/_convenience_scripts/md_toc.py

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+# Sebastian Raschka, 2015
+# convenience function for myself to create nested TOC lists
+# use as `python md_toc.py /blank_tocs/ch01.toc`
+
+import sys
+
+ipynb = sys.argv[1]
+with open(ipynb, 'r') as f:
+    for line in f:
+        out_str = ' ' * (len(line) - len(line.lstrip()))
+        line = line.strip()
+        out_str += '- %s' % line
+        print(out_str)