#!/usr/bin/env python
# coding: utf-8
# [
](http://learning.ml)
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#
# # Learning ML
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# A practical guide to understanding and applying machine learning algorithms in the quest to become a 🦄.
#
# by **[@blairhudson](http://twitter.com/blairhudson)**
#
# *This book is a work in progress. Follow [@Learning_ML](http://twitter.com/Learning_ML) for the latest updates! For detailed changes, see [commits](https://github.com/blairhudson/learningml/commits/master) on GitHub.*
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# ## Table of Contents
# ---
#
# ### [1. Introduction](01.00-Introduction.ipynb)
# * [1.01 Help](01.01-Help.ipynb)
# * [1.02 Getting Started](01.02-Getting-Started.ipynb)
#
# ### [2. Classification](02.00-Classification.ipynb)
# * [2.01 Dummy Classifiers](02.01-Dummy-Classifiers.ipynb)
# * [2.02 Naive Bayes](02.02-Naive-Bayes.ipynb)
# * [2.03 k-Nearest Neighbours](02.03-k-Nearest-Neighbours.ipynb)
# * [2.04 Decision Trees](02.04-Decision-Trees.ipynb)
# * Logistic Regression
# * Support Vector Machines
# * Elastic Net
# * Stochastic Gradient Descent
# * RuleFit
# * Ensembles
# * Neural Networks
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# ### [3. Regression](03.00-Regression.ipynb)
# * Linear Regression
# * Support Vector Regression
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# ### [4. Unsupervised](04.00-Unsupervised.ipynb)
# * k-means
# * t-SNE
# * Apriori
# * PCA
# * LDA
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# ### [5. Deep Learning](05.00-Deep-Learning.ipynb)
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# * Keras and TensorFlow
# * Deep Neural Networks: Classification and Regression
# * DNN Problems and Architectures
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# ### [6. Big Data](06.00-Big-Data.ipynb)
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# * Spark MLLib
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# ### [Appendix](99.00-Appendix.ipynb)
# * [a. Glossary](99.01-Glossary.ipynb)
# * [b. Acknowledgements](99.02-Acknowledgements.ipynb)
# * [c. Resources](99.03-Resources.ipynb)
# ---
#
# [learning.ml](http://learning.ml) / This work is licensed under [CC BY-NC-ND 3.0 AU](https://creativecommons.org/licenses/by-nc-nd/3.0/au/).
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