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‎code/_convenience_scripts/blank_tocs/ch01.toc

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

‎code/_convenience_scripts/blank_tocs/ch02.toc

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

‎code/_convenience_scripts/blank_tocs/ch03.toc

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

‎code/_convenience_scripts/ipynb_toc_links.py

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

‎code/ch01/ch01.ipynb

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"- [Summary](#Summary)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Building intelligent machines to transform data into knowledge"
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"# Building intelligent machines to transform data into knowledge"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The three different types of machine learning"
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"# The three different types of machine learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Making predictions about the future with supervised learning"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Making predictions about the future with supervised learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Classification for predicting class labels"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Classification for predicting class labels"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Regression for predicting continuous outcomes"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Regression for predicting continuous outcomes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Solving interactive problems with reinforcement learning"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Solving interactive problems with reinforcement learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Discovering hidden structures with unsupervised learning"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Discovering hidden structures with unsupervised learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Finding subgroups with clustering"
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"..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Finding subgroups with clustering"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Dimensionality reduction for data compression"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Dimensionality reduction for data compression"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### An introduction to the basic terminology and notations"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### An introduction to the basic terminology and notations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## A roadmap for building machine learning systems"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# A roadmap for building machine learning systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Preprocessing - getting data into shape"
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Training and selecting a predictive model"
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"## Preprocessing - getting data into shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Evaluating models and predicting unseen data instances"
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"..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using Python for machine learning"
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"## Training and selecting a predictive model"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installing Python packages"
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"..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Summary"
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"## Evaluating models and predicting unseen data instances"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Using Python for machine learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Installing Python packages"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Summary"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"..."
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]
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}
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],
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"metadata": {

‎code/ch02/README.md

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Sebastian Raschka, 2015
22

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# Python Machine Learning - Chapter 2 Code
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The content is organized as follows:
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- `code/`: the main IPython notebook with the code example as they appear in the chapter.
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- `data/`: optional offline version(s) of the dataset(s)
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- `figures/`: notebook figures and color figures
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# Python Machine Learning - Chapter 2 Code Examples

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