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‎code/README.md

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## Table of Contents and Code Notebooks
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Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version).
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**Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**
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1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./ch01)] [[ipynb](./ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
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2. Training Machine Learning Algorithms for Classification [[dir](./ch02)] [[ipynb](./ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
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3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./ch03)] [[ipynb](./ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
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4. Building Good Training Sets – Data Pre-Processing [[dir](./ch04)] [[ipynb](./ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
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5. Compressing Data via Dimensionality Reduction [[dir](./ch05)] [[ipynb](./ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)]
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6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./ch06)] [[ipynb](./ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
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7. Combining Different Models for Ensemble Learning [[dir](./ch07)] [[ipynb](./ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
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8. Applying Machine Learning to Sentiment Analysis [[dir](./ch08)] [[ipynb](./ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
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9. Embedding a Machine Learning Model into a Web Application [[dir](./ch09)] [[ipynb](./ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
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10. Predicting Continuous Target Variables with Regression Analysis [[dir](./ch10)] [[ipynb](./ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
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11. Working with Unlabeled Data – Clustering Analysis [[dir](./ch11)] [[ipynb](./ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
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12. Training Artificial Neural Networks for Image Recognition [[dir](./ch12)] [[ipynb](./ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
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13. Parallelizing Neural Network Training via Theano [[dir](./ch13)] [[ipynb](.2/ch12.ipynb)]
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13. Parallelizing Neural Network Training via Theano [[dir](./ch13)] [[ipynb](./ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
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## Contact
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I am happy to answer questions! Just write me an [email](mailto:[email protected])
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or consider asking the question on the [Google Groups Email List](https://groups.google.com/forum/#!forum/python-machine-learning-book).
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If you are interested in keeping in touch, I have quite a lively twitter stream ([@rasbt](https://twitter.com/rasbt)) all about data science and machine learning. I also maintain a [blog](http://sebastianraschka.com/articles.html) where I post all of the things I am particularly excited about.

‎code/_convenience_scripts/md_toc.py

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# Sebastian Raschka, 2015
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# convenience function for myself to create nested TOC lists
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# use as `python md_toc.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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print(out_str)

‎code/ch01/README.md

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Sebastian Raschka, 2015
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# Python Machine Learning
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Python Machine Learning - Code Examples
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# Chapter 1 Code Examples
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## Giving Computers the Ability to Learn from Data
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## Chapter 1 - Giving Computers the Ability to Learn from Data
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<hr>
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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
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---
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**Chapter 1 does not contain any code examples.**
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<hr>
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---
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## Installing Python packages
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‎code/ch02/README.md

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Sebastian Raschka, 2015
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# Python Machine Learning
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# Chapter 2 Code Examples
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Python Machine Learning - Code Examples
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## Training Machine Learning Algorithms for Classification
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## Chapter 2 - Training Machine Learning Algorithms for Classification
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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/ch03/README.md

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Sebastian Raschka, 2015
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# Python Machine Learning
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# Chapter 3 Code Examples
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Python Machine Learning - Code Examples
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## A Tour of Machine Learning Classifiers Using Scikit-learn
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## Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-learn
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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/ch04/README.md

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Sebastian Raschka, 2015
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# Python Machine Learning
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# Chapter 4 Code Examples
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Python Machine Learning - Code Examples
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## Building Good Training Sets – Data Preprocessing
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## Chapter 4 - Building Good Training Sets – Data Preprocessing
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- Dealing with missing data
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- Eliminating samples or features with missing values
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- Imputing missing values
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- Understanding the scikit-learn estimator API
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- Handling categorical data
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- Mapping ordinal features
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- Encoding class labels
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- Performing one-hot encoding on nominal features
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- Partitioning a dataset in training and test sets
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- Bringing features onto the same scale
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- Selecting meaningful features
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- Sparse solutions with L1 regularization
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- Sequential feature selection algorithms
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- Assessing feature importance with random forests
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- Summary

‎code/ch05/README.md

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Sebastian Raschka, 2015
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# Python Machine Learning
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# Chapter 5 Code Examples
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Python Machine Learning - Code Examples
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## Compressing Data via Dimensionality Reduction
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## Chapter 5 - Compressing Data via Dimensionality Reduction
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- Unsupervised dimensionality reduction via principal component analysis 128
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- Total and explained variance
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- Feature transformation
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- Principal component analysis in scikit-learn
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- Supervised data compression via linear discriminant analysis
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- Computing the scatter matrices
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- Selecting linear discriminants for the new feature subspace
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- Projecting samples onto the new feature space
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- LDA via scikit-learn
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- Using kernel principal component analysis for nonlinear mappings
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- Kernel functions and the kernel trick
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- Implementing a kernel principal component analysis in Python
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- Example 1 – separating half-moon shapes
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- Example 2 – separating concentric circles
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- Projecting new data points
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- Kernel principal component analysis in scikit-learn
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- Summary

‎code/ch06/README.md

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Sebastian Raschka, 2015
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# Python Machine Learning
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Python Machine Learning - Code Examples
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# Chapter 6 Code Examples
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## Learning Best Practices for Model Evaluation and Hyperparameter Tuning
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## Chapter 6 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning
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- Streamlining workflows with pipelines
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- Loading the Breast Cancer Wisconsin dataset
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- Combining transformers and estimators in a pipeline
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- Using k-fold cross-validation to assess model performance
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- The holdout method
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- K-fold cross-validation
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- Debugging algorithms with learning and validation curves
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- Diagnosing bias and variance problems with learning curves
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- Addressing overfitting and underfitting with validation curves
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- Fine-tuning machine learning models via grid search
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- Tuning hyperparameters via grid search
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- Algorithm selection with nested cross-validation
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- Looking at different performance evaluation metrics
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- Reading a confusion matrix
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- Optimizing the precision and recall of a classification model
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- Plotting a receiver operating characteristic
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- The scoring metrics for multiclass classification
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- Summary
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‎code/ch07/README.md

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Sebastian Raschka, 2015
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# Python Machine Learning
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# Chapter 7 Code Examples
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Python Machine Learning - Code Examples
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## Combining Different Models for Ensemble Learning
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## Chapter 7 - Combining Different Models for Ensemble Learning
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- Learning with ensembles
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- Implementing a simple majority vote classifier
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- Combining different algorithms for classification with majority vote
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- Evaluating and tuning the ensemble classifier
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- Bagging – building an ensemble of classifiers from bootstrap samples
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- Leveraging weak learners via adaptive boosting
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- Summary

‎code/ch08/README.md

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# Python Machine Learning
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# Chapter 8 Code Examples
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Python Machine Learning - Code Examples
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## Applying Machine Learning to Sentiment Analysis
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## Chapter 8 - Applying Machine Learning to Sentiment Analysis
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- Obtaining the IMDb movie review dataset
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- Introducing the bag-of-words model
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- Transforming words into feature vectors
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- Assessing word relevancy via term frequency-inverse document frequency
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- Cleaning text data
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- Processing documents into tokens
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- Training a logistic regression model for document classification
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- Working with bigger data – online algorithms and out-of-core learning
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- Summary

‎code/ch09/README.md

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# Python Machine Learning
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# Chapter 9 Code Examples
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Python Machine Learning - Code Examples
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## Embedding a Machine Learning Model into a Web Application
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## Chapter 9 - Embedding a Machine Learning Model into a Web Application
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- Serializing fitted scikit-learn estimators
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- Setting up a SQLite database for data storage
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- Developing a web application with Flask
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- Our first Flask web application
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- Form validation and rendering
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- Turning the movie classifier into a web application
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- Deploying the web application to a public server
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- Updating the movie review classifier
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- Summary
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---
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The code for the Flask web applications can be found in the following directories:
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‎code/ch10/README.md

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# Python Machine Learning
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# Chapter 10 Code Examples
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Python Machine Learning - Code Examples
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## Predicting Continuous Target Variables with Regression Analysis
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## Chapter 10 - Predicting Continuous Target Variables with Regression Analysis
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- Introducing a simple linear regression model
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- Exploring the Housing Dataset
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- Visualizing the important characteristics of a dataset
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- Implementing an ordinary least squares linear regression model
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- Solving regression for regression parameters with gradient descent
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- Estimating the coefficient of a regression model via scikit-learn
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- Fitting a robust regression model using RANSAC
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- Evaluating the performance of linear regression models
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- Using regularized methods for regression
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- Turning a linear regression model into a curve – polynomial regression
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- Modeling nonlinear relationships in the Housing Dataset
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- Dealing with nonlinear relationships using random forests
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- Decision tree regression
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- Random forest regression
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- Summary

‎code/ch11/README.md

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# Python Machine Learning
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# Chapter 11 Code Examples
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Python Machine Learning - Code Examples
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## Working with Unlabeled Data – Clustering Analysis
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## Chapter 11 - Working with Unlabeled Data – Clustering Analysis
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- Grouping objects by similarity using k-means
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- K-means++
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- Hard versus soft clustering
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- Using the elbow method to find the optimal number of clusters
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- Quantifying the quality of clustering via silhouette plots
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- Organizing clusters as a hierarchical tree
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- Performing hierarchical clustering on a distance matrix
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- Attaching dendrograms to a heat map
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- Applying agglomerative clustering via scikit-learn
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- Locating regions of high density via DBSCAN
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- Summary

‎code/ch12/README.md

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# Python Machine Learning
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# Chapter 12 Code Examples
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Python Machine Learning - Code Examples
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## Training Artificial Neural Networks for Image
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## Chapter 12 - Training Artificial Neural Networks for Image
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- Modeling complex functions with artificial neural networks
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- Single-layer neural network recap
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- Introducing the multi-layer neural network architecture
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- Activating a neural network via forward propagation
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- Classifying handwritten digits
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- Obtaining the MNIST dataset
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- Implementing a multi-layer perceptron
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- Training an artificial neural network
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- Computing the logistic cost function
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- Training neural networks via backpropagation
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- Developing your intuition for backpropagation
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- Debugging neural networks with gradient checking
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- Convergence in neural networks
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- Other neural network architectures
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- Convolutional Neural Networks
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- Recurrent Neural Networks
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- A few last words about neural network implementation
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- Summary

‎code/ch13/README.md

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# Python Machine Learning
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# Chapter 13 Code Examples
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Python Machine Learning - Code Examples
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## Parallelizing Neural Network Training with Theano
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## Chapter 13 - Parallelizing Neural Network Training with Theano
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- Building, compiling, and running expressions with Theano
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- What is Theano?
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- First steps with Theano
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- Configuring Theano
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- Working with array structures
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- Wrapping things up – a linear regression example
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- Choosing activation functions for feedforward neural networks
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- Logistic function recap
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- Estimating probabilities in multi-class classification via the softmax function
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- Broadening the output spectrum by using a hyperbolic tangent
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- Training neural networks efficiently using Keras
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- Summary

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