{ "metadata": { "name": "", "signature": "sha256:bc2860f990730fd6dabc20dd32ddeeb48f989228997eca69c76980b706d56946" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PyData Seattle 2015 Scikit-Learn Tutorial\n", "\n", "This is the main index of the PyData Seattle 2015 Introduction to Scikit-Learn tutorial, presented by [Jake VanderPlas](http://www.vanderplas.com).\n", "Please refer to the [github repository](http://github.com/jakevdp/sklearn_pydata2015) for this tutorial for any updates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial Notebooks\n", "\n", "The following links are to notebooks containing the tutorial materials.\n", "Note that many of these require files that are in the directory structure of the [github repository](http://github.com/jakevdp/sklearn_pydata2015) in which they are contained.\n", "There is not time during the tutorial to cover all of this material, but I left it in in case attendees would like to go deeper on their own.\n", "\n", "### 1. Preliminaries\n", "\n", " + [01-Preliminaries.ipynb](01-Preliminaries.ipynb)\n", " \n", "### 2. Introduction to Machine Learning with Scikit-Learn\n", "\n", " + [02.1-Machine-Learning-Intro.ipynb](02.1-Machine-Learning-Intro.ipynb)\n", " + [02.2-Basic-Principles.ipynb](02.2-Basic-Principles.ipynb)\n", " \n", "### 3. Supervised Learning In-Depth\n", "\n", " + [03.1-Classification-SVMs.ipynb](03.1-Classification-SVMs.ipynb)\n", " + [03.2-Regression-Forests.ipynb](03.2-Regression-Forests.ipynb)\n", "\n", "### 4. Unsupervised Learning In-Depth\n", "\n", " + [04.1-Dimensionality-PCA.ipynb](04.1-Dimensionality-PCA.ipynb)\n", " + [04.2-Clustering-KMeans.ipynb](04.2-Clustering-KMeans.ipynb)\n", " + [04.3-Density-GMM.ipynb](04.3-Density-GMM.ipynb)\n", " \n", "### 5. Model Validation In-Depth\n", "\n", " + [05-Validation.ipynb](05-Validation.ipynb)" ] } ], "metadata": {} } ] }