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‎01-Preliminaries.ipynb

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{
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"cells": [
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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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"<small><i>This notebook was put together by [Jake Vanderplas](http://www.vanderplas.com). Source and license info is on [GitHub](https://github.com/jakevdp/sklearn_tutorial/).</i></small>"
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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 scikit-learn: Machine Learning in Python"
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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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"## Goals of this Tutorial"
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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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"- **Introduce the basics of Machine Learning**, and some skills useful in practice.\n",
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"- **Introduce the syntax of scikit-learn**, so that you can make use of the rich toolset available."
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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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"## Schedule:"
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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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"**Preliminaries: Setup & introduction** (15 min)\n",
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"* Making sure your computer is set-up\n",
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"\n",
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"**Basic Principles of Machine Learning and the Scikit-learn Interface** (45 min)\n",
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"* What is Machine Learning?\n",
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"* Machine learning data layout\n",
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"* Supervised Learning\n",
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" - Classification\n",
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" - Regression\n",
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" - Measuring performance\n",
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"* Unsupervised Learning\n",
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" - Clustering\n",
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" - Dimensionality Reduction\n",
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" - Density Estimation\n",
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"* Evaluation of Learning Models\n",
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"* Choosing the right algorithm for your dataset\n",
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"\n",
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"**Supervised learning in-depth** (1 hr)\n",
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"* Support Vector Machines\n",
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"* Decision Trees and Random Forests\n",
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"\n",
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"**Unsupervised learning in-depth** (1 hr)\n",
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"* Principal Component Analysis\n",
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"* K-means Clustering\n",
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"* Gaussian Mixture Models\n",
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"\n",
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"**Model Validation** (1 hr)\n",
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"* Validation and Cross-validation"
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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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"## Preliminaries"
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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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"This tutorial requires the following packages:\n",
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"\n",
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"- Python version 2.7 or 3.4+\n",
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"- `numpy` version 1.8 or later: http://www.numpy.org/\n",
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"- `scipy` version 0.15 or later: http://www.scipy.org/\n",
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"- `matplotlib` version 1.3 or later: http://matplotlib.org/\n",
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"- `scikit-learn` version 0.15 or later: http://scikit-learn.org\n",
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"- `ipython`/`jupyter` version 3.0 or later, with notebook support: http://ipython.org\n",
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"- `seaborn`: version 0.5 or later, used mainly for plot styling\n",
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"\n",
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"The easiest way to get these is to use the [conda](http://store.continuum.io/) environment manager.\n",
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"I suggest downloading and installing [miniconda](http://conda.pydata.org/miniconda.html).\n",
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"\n",
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"The following command will install all required packages:\n",
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"```\n",
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"$ conda install numpy scipy matplotlib scikit-learn ipython-notebook\n",
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"```\n",
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"\n",
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"Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/."
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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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"### Checking your installation\n",
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"\n",
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"You can run the following code to check the versions of the packages on your system:\n",
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"\n",
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"(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"IPython: 6.2.1\n",
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"numpy: 1.13.3\n",
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"scipy: 1.0.0\n",
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"matplotlib: 2.2.2\n",
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"scikit-learn: 0.19.1\n"
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]
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}
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],
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"source": [
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"from __future__ import print_function\n",
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"\n",
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"import IPython\n",
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"print('IPython:', IPython.__version__)\n",
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"\n",
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"import numpy\n",
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"print('numpy:', numpy.__version__)\n",
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"\n",
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"import scipy\n",
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"print('scipy:', scipy.__version__)\n",
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"\n",
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"import matplotlib\n",
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"print('matplotlib:', matplotlib.__version__)\n",
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"\n",
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"import sklearn\n",
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"print('scikit-learn:', sklearn.__version__)"
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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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"## Useful Resources"
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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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"- **scikit-learn:** http://scikit-learn.org (see especially the narrative documentation)\n",
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"- **matplotlib:** http://matplotlib.org (see especially the gallery section)\n",
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"- **Jupyter:** http://jupyter.org (also check out http://nbviewer.jupyter.org)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎02.1-Machine-Learning-Intro.ipynb

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