setup instructions

code/README.md

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+## Resources for setting up your coding environment
+
+- [Instructions for setting up Python and the Jupyter Notebook](./ch01/README.md)  
+
+- A [quick NumPy introduction/refresher](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.pdf) that covers everything (and more) you'd need for this book
+
+## Table of contents and code notebooks
+
+
+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).
+**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.**   
+
+
+
+<br>
+
+
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+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)]
+
+<br>
+
+**Bonus Notebooks (not in the book)**
+
+- Logistic Regression Implementation [[dir](./bonus)] [[ipynb](./bonus/logistic_regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/logistic_regression.ipynb)]
+- A Basic Pipeline and Grid Search Setup [[dir](./bonus)] [[ipynb](./bonus/svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
+- An Extended Nested Cross-Validation Example [[dir](./bonus)] [[ipynb](./bonus/nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]
+- A Simple(r) Barebones Flask Webapp Template [[view directory](./bonus/flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
+- Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./bonus/reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
+- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./bonus/scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
+- Multinomial logistic regression / softmax regression [[GitHub ipynb](./bonus/softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
+
+
+
+
+## Contact
+
+I am happy to answer questions! Just write me an [email](mailto:[email protected])
+or consider asking the question on the [Google Groups Email List](https://groups.google.com/forum/#!forum/python-machine-learning-book).
+
+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/ch01/README.md

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+Sebastian Raschka, 2017
+
+Python Machine Learning - Code Examples
+
+
+##  Chapter 1 - Giving Computers the Ability to Learn from Data
+
+
+---
+
+**Chapter 1 does not contain any code examples.**
+
+---
+
+## Installing Python packages
+
+Python is available for all three major operating systems — Microsoft Windows, macOS, and Linux — and the installer, as well as the documentation, can be downloaded from the official Python website: https://www.python.org.
+
+This book is written for Python version `>= 3.6.0`, and it is recommended
+you use the most recent version of Python 3 that is currently available,
+although most of the code examples may also be compatible with older versions of Python 3 and Python `>= 2.7.10`. If you decide to use Python 2.7 to execute the code examples, please make sure that you know about the major differences between the two Python versions. A good summary about the differences between Python 3 and 2.7 can be found at https://wiki.python.org/moin/Python2orPython3.
+
+**Note**
+
+You can check your current default version of Python by executing
+
+    $ python -V
+
+In my case, it returns
+
+    Python 3.6.1 :: Continuum Analytics, Inc.
+
+
+#### Pip
+
+The additional packages that we will be using throughout this book can be installed via the `pip` installer program, which has been part of the Python standard library since Python 3.3. More information about pip can be found at https://docs.python.org/3/installing/index.html.
+
+After we have successfully installed Python, we can execute pip from the command line terminal to install additional Python packages:
+
+    pip install SomePackage
+
+
+(where `SomePackage` is a placeholder for numpy, pandas, matplotlib, scikit-learn, and so forth).
+
+Already installed packages can be updated via the `--upgrade` flag:
+
+    pip install SomePackage --upgrade
+
+
+#### Anaconda
+
+A highly recommended alternative Python distribution for scientific computing
+is Anaconda by Continuum Analytics. Anaconda is a free—including commercial use—enterprise-ready Python distribution that bundles all the essential Python packages for data science, math, and engineering in one user-friendly cross-platform distribution. The Anaconda installer can be downloaded at http://continuum.io/downloads#py34, and an Anaconda quick start-guide is available at https://store.continuum.io/static/img/Anaconda-Quickstart.pdf.
+
+After successfully installing Anaconda, we can install new Python packages using the following command:
+
+    conda install SomePackage
+
+Existing packages can be updated using the following command:
+
+    conda update SomePackage
+
+Throughout this book, we will mainly use NumPy's multi-dimensional arrays to store and manipulate data. Occasionally, we will make use of pandas, which is a library built on top of NumPy that provides additional higher level data manipulation tools that make working with tabular data even more convenient. To augment our learning experience and visualize quantitative data, which is often extremely useful to intuitively make sense of it, we will use the very customizable matplotlib library.
+
+#### Core packages
+
+The version numbers of the major Python packages that were used for writing this book are listed below. Please make sure that the version numbers of your installed packages are equal to, or greater than, those version numbers to ensure the code examples run correctly:
+
+- [NumPy](http://www.numpy.org) >= 1.12.1
+- [SciPy](http://www.scipy.org) >= 0.19.0
+- [scikit-learn](http://scikit-learn.org/stable/) >= 0.18.1
+- [matplotlib](http://matplotlib.org) >= 2.0.2
+- [pandas](http://pandas.pydata.org) >= 0.20.1
+
+## Python/Jupyter Notebook
+
+Some readers were wondering about the `.ipynb` of the code files -- these files are IPython notebooks. I chose IPython notebooks over plain Python `.py` scripts, because I think that they are just great for data analysis projects! IPython notebooks allow us to have everything in one place: Our code, the results from executing the code, plots of our data, and documentation that supports the handy Markdown and powerful LaTeX syntax!
+
+![](./images/ipynb_ex1.png)
+
+**Side Note:**  "IPython Notebook" recently became the "[Jupyter Notebook](<http://jupyter.org>)"; Jupyter is an umbrella project that aims to support other languages in addition to Python including Julia, R, and many more. Don't worry, though, for a Python user, there's only a difference in terminology (we say "Jupyter Notebook" now instead of "IPython Notebook").
+
+The Jupyter notebook can be installed as usually via pip.
+
+    $ pip install jupyter notebook
+
+Alternatively, we can use the Conda installer if we have Anaconda or Miniconda installed:
+
+    $ conda install jupyter notebook
+
+To open a Jupyter notebook, we `cd` to the directory that contains your code examples, e.g,.
+
+
+    $ cd ~/code/python-machine-learning-book
+
+and launch `jupyter notebook` by executing
+
+    $ jupyter notebook
+
+Jupyter will start in our default browser (typically running at [http://localhost:8888/](http://localhost:8888/)). Now, we can simply select the notebook you wish to open from the Jupyter menu.
+
+![](./images/ipynb_ex2.png)
+
+For more information about the Jupyter notebook, I recommend the [Jupyter Beginner Guide](http://jupyter-notebook-beginner-guide.readthedocs.org/en/latest/what_is_jupyter.html).