convert notebooks to v4, update links, make compatible with python 3 and sklearn 0.17
Kevin Markham
parent
fd679c9d87
commit
0b6a8a05ab
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@ -1,248 +1,260 @@
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{
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# What is machine learning, and how does it work?\n",
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"*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
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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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""
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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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"## Agenda\n",
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"\n",
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"- What is machine learning?\n",
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"- What are the two main categories of machine learning?\n",
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"- What are some examples of machine learning?\n",
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"- How does machine learning \"work\"?"
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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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"## What is machine learning?\n",
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"\n",
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"One definition: \"Machine learning is the semi-automated extraction of knowledge from data\"\n",
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"\n",
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"- **Knowledge from data**: Starts with a question that might be answerable using data\n",
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"- **Automated extraction**: A computer provides the insight\n",
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"- **Semi-automated**: Requires many smart decisions by a human"
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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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"## What are the two main categories of machine learning?\n",
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"\n",
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"**Supervised learning**: Making predictions using data\n",
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" \n",
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"- Example: Is a given email \"spam\" or \"ham\"?\n",
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"- There is an outcome we are trying to predict"
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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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""
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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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"**Unsupervised learning**: Extracting structure from data\n",
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"\n",
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"- Example: Segment grocery store shoppers into clusters that exhibit similar behaviors\n",
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"- There is no \"right answer\""
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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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""
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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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"## How does machine learning \"work\"?\n",
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"\n",
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"High-level steps of supervised learning:\n",
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"\n",
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"1. First, train a **machine learning model** using **labeled data**\n",
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"\n",
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||||
" - \"Labeled data\" has been labeled with the outcome\n",
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" - \"Machine learning model\" learns the relationship between the attributes of the data and its outcome\n",
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"\n",
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"2. Then, make **predictions** on **new data** for which the label is unknown"
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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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""
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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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"The primary goal of supervised learning is to build a model that \"generalizes\": It accurately predicts the **future** rather than the **past**!"
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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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"## Questions about machine learning\n",
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"\n",
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"- How do I choose **which attributes** of my data to include in the model?\n",
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"- How do I choose **which model** to use?\n",
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"- How do I **optimize** this model for best performance?\n",
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"- How do I ensure that I'm building a model that will **generalize** to unseen data?\n",
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"- Can I **estimate** how well my model is likely to perform on unseen data?"
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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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"## Resources\n",
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"\n",
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"- Book: [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (section 2.1, 14 pages)\n",
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"- Video: [Learning Paradigms](http://work.caltech.edu/library/014.html) (13 minutes)"
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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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"## Comments or Questions?\n",
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"\n",
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"- Email: <kevin@dataschool.io>\n",
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"- Website: http://dataschool.io\n",
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"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
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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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"collapsed": false
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},
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"outputs": [
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{
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"cell_type": "markdown",
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"data": {
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"text/html": [
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"<style>\n",
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" @font-face {\n",
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" font-family: \"Computer Modern\";\n",
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" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
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" }\n",
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" div.cell{\n",
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" width: 90%;\n",
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"/* margin-left:auto;*/\n",
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"/* margin-right:auto;*/\n",
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" }\n",
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" ul {\n",
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" line-height: 145%;\n",
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" font-size: 90%;\n",
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" }\n",
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" li {\n",
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" margin-bottom: 1em;\n",
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" }\n",
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" h1 {\n",
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" font-family: Helvetica, serif;\n",
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" }\n",
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" h4{\n",
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" margin-top: 12px;\n",
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" margin-bottom: 3px;\n",
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" }\n",
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" div.text_cell_render{\n",
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" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
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" line-height: 145%;\n",
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" font-size: 130%;\n",
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" width: 90%;\n",
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" margin-left:auto;\n",
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" margin-right:auto;\n",
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" }\n",
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" .CodeMirror{\n",
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" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
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" }\n",
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"/* .prompt{\n",
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" display: None;\n",
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" }*/\n",
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" .text_cell_render h5 {\n",
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" font-weight: 300;\n",
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" font-size: 16pt;\n",
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" color: #4057A1;\n",
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" font-style: italic;\n",
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" margin-bottom: 0.5em;\n",
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" margin-top: 0.5em;\n",
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" display: block;\n",
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" }\n",
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"\n",
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" .warning{\n",
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" color: rgb( 240, 20, 20 )\n",
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" }\n",
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"</style>\n",
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"<script>\n",
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" MathJax.Hub.Config({\n",
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" TeX: {\n",
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" extensions: [\"AMSmath.js\"]\n",
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" },\n",
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" tex2jax: {\n",
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" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
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" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
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" },\n",
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" displayAlign: 'center', // Change this to 'center' to center equations.\n",
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" \"HTML-CSS\": {\n",
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" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
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" }\n",
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" });\n",
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"</script>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"execution_count": 1,
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||||
"metadata": {},
|
||||
"source": [
|
||||
"# What is machine learning, and how does it work?\n",
|
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"*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
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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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""
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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": [
|
||||
"## Agenda\n",
|
||||
"\n",
|
||||
"- What is machine learning?\n",
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"- What are the two main categories of machine learning?\n",
|
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"- What are some examples of machine learning?\n",
|
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"- How does machine learning \"work\"?"
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]
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},
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||||
{
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## What is machine learning?\n",
|
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"\n",
|
||||
"One definition: \"Machine learning is the semi-automated extraction of knowledge from data\"\n",
|
||||
"\n",
|
||||
"- **Knowledge from data**: Starts with a question that might be answerable using data\n",
|
||||
"- **Automated extraction**: A computer provides the insight\n",
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"- **Semi-automated**: Requires many smart decisions by a human"
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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": [
|
||||
"## What are the two main categories of machine learning?\n",
|
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"\n",
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"**Supervised learning**: Making predictions using data\n",
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" \n",
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"- Example: Is a given email \"spam\" or \"ham\"?\n",
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"- There is an outcome we are trying to predict"
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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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""
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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": [
|
||||
"**Unsupervised learning**: Extracting structure from data\n",
|
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"\n",
|
||||
"- Example: Segment grocery store shoppers into clusters that exhibit similar behaviors\n",
|
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"- There is no \"right answer\""
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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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""
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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": [
|
||||
"## How does machine learning \"work\"?\n",
|
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"\n",
|
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"High-level steps of supervised learning:\n",
|
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"\n",
|
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"1. First, train a **machine learning model** using **labeled data**\n",
|
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"\n",
|
||||
" - \"Labeled data\" has been labeled with the outcome\n",
|
||||
" - \"Machine learning model\" learns the relationship between the attributes of the data and its outcome\n",
|
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"\n",
|
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"2. Then, make **predictions** on **new data** for which the label is unknown"
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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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""
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]
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},
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||||
{
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"The primary goal of supervised learning is to build a model that \"generalizes\": It accurately predicts the **future** rather than the **past**!"
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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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"## Questions about machine learning\n",
|
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"\n",
|
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"- How do I choose **which attributes** of my data to include in the model?\n",
|
||||
"- How do I choose **which model** to use?\n",
|
||||
"- How do I **optimize** this model for best performance?\n",
|
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"- How do I ensure that I'm building a model that will **generalize** to unseen data?\n",
|
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"- Can I **estimate** how well my model is likely to perform on unseen data?"
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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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"## Resources\n",
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"\n",
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"- Book: [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (section 2.1, 14 pages)\n",
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"- Video: [Learning Paradigms](http://work.caltech.edu/library/014.html) (13 minutes)"
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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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"## Comments or Questions?\n",
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"\n",
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"- Email: <kevin@dataschool.io>\n",
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"- Website: http://dataschool.io\n",
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"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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||||
"from IPython.core.display import HTML\n",
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"def css_styling():\n",
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" styles = open(\"styles/custom.css\", \"r\").read()\n",
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" return HTML(styles)\n",
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"css_styling()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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||||
"html": [
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||||
"<style>\n",
|
||||
" @font-face {\n",
|
||||
" font-family: \"Computer Modern\";\n",
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||||
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
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" }\n",
|
||||
" div.cell{\n",
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" width: 90%;\n",
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"/* margin-left:auto;*/\n",
|
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"/* margin-right:auto;*/\n",
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||||
" }\n",
|
||||
" ul {\n",
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||||
" line-height: 145%;\n",
|
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" font-size: 90%;\n",
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" }\n",
|
||||
" li {\n",
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||||
" margin-bottom: 1em;\n",
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||||
" }\n",
|
||||
" h1 {\n",
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" font-family: Helvetica, serif;\n",
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" }\n",
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" h4{\n",
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" margin-top: 12px;\n",
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" margin-bottom: 3px;\n",
|
||||
" }\n",
|
||||
" div.text_cell_render{\n",
|
||||
" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
|
||||
" line-height: 145%;\n",
|
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" font-size: 130%;\n",
|
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" width: 90%;\n",
|
||||
" margin-left:auto;\n",
|
||||
" margin-right:auto;\n",
|
||||
" }\n",
|
||||
" .CodeMirror{\n",
|
||||
" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
|
||||
" }\n",
|
||||
"/* .prompt{\n",
|
||||
" display: None;\n",
|
||||
" }*/\n",
|
||||
" .text_cell_render h5 {\n",
|
||||
" font-weight: 300;\n",
|
||||
" font-size: 16pt;\n",
|
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" color: #4057A1;\n",
|
||||
" font-style: italic;\n",
|
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" margin-bottom: 0.5em;\n",
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" margin-top: 0.5em;\n",
|
||||
" display: block;\n",
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" }\n",
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"\n",
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" .warning{\n",
|
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" color: rgb( 240, 20, 20 )\n",
|
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" }\n",
|
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"</style>\n",
|
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"<script>\n",
|
||||
" MathJax.Hub.Config({\n",
|
||||
" TeX: {\n",
|
||||
" extensions: [\"AMSmath.js\"]\n",
|
||||
" },\n",
|
||||
" tex2jax: {\n",
|
||||
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
|
||||
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
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" },\n",
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" displayAlign: 'center', // Change this to 'center' to center equations.\n",
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" \"HTML-CSS\": {\n",
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||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
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||||
" }\n",
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" });\n",
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||||
"</script>"
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||||
],
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||||
"metadata": {},
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||||
"output_type": "pyout",
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||||
"prompt_number": 1,
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||||
"text": [
|
||||
"<IPython.core.display.HTML at 0x3edad30>"
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]
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||||
}
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],
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"prompt_number": 1
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"output_type": "execute_result"
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}
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],
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||||
"metadata": {}
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||||
"source": [
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"from IPython.core.display import HTML\n",
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"def css_styling():\n",
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" styles = open(\"styles/custom.css\", \"r\").read()\n",
|
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" return HTML(styles)\n",
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"css_styling()"
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]
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}
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]
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||||
}
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],
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"metadata": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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"name": "ipython",
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"file_extension": ".py",
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|
@ -1,258 +1,270 @@
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|
||||
"# Setting up Python for machine learning: scikit-learn and IPython Notebook\n",
|
||||
"*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
|
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|
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|
||||
{
|
||||
"cell_type": "markdown",
|
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"metadata": {},
|
||||
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|
||||
"## Agenda\n",
|
||||
"\n",
|
||||
"- What are the benefits and drawbacks of scikit-learn?\n",
|
||||
"- How do I install scikit-learn?\n",
|
||||
"- How do I use the IPython Notebook?\n",
|
||||
"- What are some good resources for learning Python?"
|
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]
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{
|
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"cell_type": "markdown",
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""
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|
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{
|
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"cell_type": "markdown",
|
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"metadata": {},
|
||||
"source": [
|
||||
"## Benefits and drawbacks of scikit-learn\n",
|
||||
"\n",
|
||||
"### Benefits:\n",
|
||||
"\n",
|
||||
"- **Consistent interface** to machine learning models\n",
|
||||
"- Provides many **tuning parameters** but with **sensible defaults**\n",
|
||||
"- Exceptional **documentation**\n",
|
||||
"- Rich set of functionality for **companion tasks**\n",
|
||||
"- **Active community** for development and support\n",
|
||||
"\n",
|
||||
"### Potential drawbacks:\n",
|
||||
"\n",
|
||||
"- Harder (than R) to **get started with machine learning**\n",
|
||||
"- Less emphasis (than R) on **model interpretability**\n",
|
||||
"\n",
|
||||
"### Further reading:\n",
|
||||
"\n",
|
||||
"- Ben Lorica: [Six reasons why I recommend scikit-learn](http://radar.oreilly.com/2013/12/six-reasons-why-i-recommend-scikit-learn.html)\n",
|
||||
"- scikit-learn authors: [API design for machine learning software](http://arxiv.org/pdf/1309.0238v1.pdf)\n",
|
||||
"- Data School: [Should you teach Python or R for data science?](http://www.dataschool.io/python-or-r-for-data-science/)"
|
||||
]
|
||||
},
|
||||
{
|
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|
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""
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|
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|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Installing scikit-learn\n",
|
||||
"\n",
|
||||
"**Option 1:** [Install scikit-learn library](http://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n",
|
||||
"\n",
|
||||
"**Option 2:** [Install Anaconda distribution](https://www.continuum.io/downloads) of Python, which includes:\n",
|
||||
"\n",
|
||||
"- Hundreds of useful packages (including scikit-learn)\n",
|
||||
"- IPython and IPython Notebook\n",
|
||||
"- conda package manager\n",
|
||||
"- Spyder IDE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
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"metadata": {},
|
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|
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""
|
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|
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},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using the IPython Notebook\n",
|
||||
"\n",
|
||||
"### Components:\n",
|
||||
"\n",
|
||||
"- **IPython interpreter:** enhanced version of the standard Python interpreter\n",
|
||||
"- **Browser-based notebook interface:** weave together code, formatted text, and plots\n",
|
||||
"\n",
|
||||
"### Installation:\n",
|
||||
"\n",
|
||||
"- **Option 1:** Install [IPython](http://ipython.org/install.html) and the [notebook](https://jupyter.readthedocs.io/en/latest/install.html)\n",
|
||||
"- **Option 2:** Included with the Anaconda distribution\n",
|
||||
"\n",
|
||||
"### Launching the Notebook:\n",
|
||||
"\n",
|
||||
"- Type **ipython notebook** at the command line to open the dashboard\n",
|
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"- Don't close the command line window while the Notebook is running\n",
|
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|
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|
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|
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|
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|
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|
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"**Edit mode** (green border)\n",
|
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"\n",
|
||||
"- **Ctrl+Enter** to run a cell\n",
|
||||
"- Switch to Command mode using **Esc**\n",
|
||||
"\n",
|
||||
"### IPython and Markdown resources:\n",
|
||||
"\n",
|
||||
"- [nbviewer](http://nbviewer.jupyter.org/): view notebooks online as static documents\n",
|
||||
"- [IPython documentation](http://ipython.readthedocs.io/en/stable/): focuses on the interpreter\n",
|
||||
"- [IPython Notebook tutorials](http://jupyter.readthedocs.io/en/latest/content-quickstart.html): in-depth introduction\n",
|
||||
"- [GitHub's Mastering Markdown](https://guides.github.com/features/mastering-markdown/): short guide with lots of examples"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Resources for learning Python\n",
|
||||
"\n",
|
||||
"- [Codecademy's Python course](https://www.codecademy.com/learn/python): browser-based, tons of exercises\n",
|
||||
"- [DataQuest](https://www.dataquest.io/): browser-based, teaches Python in the context of data science\n",
|
||||
"- [Google's Python class](https://developers.google.com/edu/python/): slightly more advanced, includes videos and downloadable exercises (with solutions)\n",
|
||||
"- [Python for Informatics](http://www.pythonlearn.com/): beginner-oriented book, includes slides and videos"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Comments or Questions?\n",
|
||||
"\n",
|
||||
"- Email: <kevin@dataschool.io>\n",
|
||||
"- Website: http://dataschool.io\n",
|
||||
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
|
||||
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|
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|
||||
"\n",
|
||||
"- What are the benefits and drawbacks of scikit-learn?\n",
|
||||
"- How do I install scikit-learn?\n",
|
||||
"- How do I use the IPython Notebook?\n",
|
||||
"- What are some good resources for learning Python?"
|
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{
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|
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{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Benefits and drawbacks of scikit-learn\n",
|
||||
"\n",
|
||||
"### Benefits:\n",
|
||||
"\n",
|
||||
"- **Consistent interface** to machine learning models\n",
|
||||
"- Provides many **tuning parameters** but with **sensible defaults**\n",
|
||||
"- Exceptional **documentation**\n",
|
||||
"- Rich set of functionality for **companion tasks**\n",
|
||||
"- **Active community** for development and support\n",
|
||||
"\n",
|
||||
"### Potential drawbacks:\n",
|
||||
"\n",
|
||||
"- Harder (than R) to **get started with machine learning**\n",
|
||||
"- Less emphasis (than R) on **model interpretability**\n",
|
||||
"\n",
|
||||
"### Further reading:\n",
|
||||
"\n",
|
||||
"- Ben Lorica: [Six reasons why I recommend scikit-learn](http://radar.oreilly.com/2013/12/six-reasons-why-i-recommend-scikit-learn.html)\n",
|
||||
"- scikit-learn authors: [API design for machine learning software](http://arxiv.org/pdf/1309.0238v1.pdf)\n",
|
||||
"- Data School: [Should you teach Python or R for data science?](http://www.dataschool.io/python-or-r-for-data-science/)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
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|
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""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Installing scikit-learn\n",
|
||||
"\n",
|
||||
"**Option 1:** [Install scikit-learn library](http://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n",
|
||||
"\n",
|
||||
"**Option 2:** [Install Anaconda distribution](https://store.continuum.io/cshop/anaconda/) of Python, which includes:\n",
|
||||
"\n",
|
||||
"- Hundreds of useful packages (including scikit-learn)\n",
|
||||
"- IPython and IPython Notebook\n",
|
||||
"- conda package manager\n",
|
||||
"- Spyder IDE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using the IPython Notebook\n",
|
||||
"\n",
|
||||
"### Components:\n",
|
||||
"\n",
|
||||
"- **IPython interpreter:** enhanced version of the standard Python interpreter\n",
|
||||
"- **Browser-based notebook interface:** weave together code, formatted text, and plots\n",
|
||||
"\n",
|
||||
"### Installation:\n",
|
||||
"\n",
|
||||
"- **Option 1:** [Install IPython and the notebook](http://ipython.org/install.html)\n",
|
||||
"- **Option 2:** Included with the Anaconda distribution\n",
|
||||
"\n",
|
||||
"### Launching the Notebook:\n",
|
||||
"\n",
|
||||
"- Type **ipython notebook** at the command line to open the dashboard\n",
|
||||
"- Don't close the command line window while the Notebook is running\n",
|
||||
"\n",
|
||||
"### Keyboard shortcuts:\n",
|
||||
"\n",
|
||||
"**Command mode** (gray border)\n",
|
||||
"\n",
|
||||
"- Create new cells above (**a**) or below (**b**) the current cell\n",
|
||||
"- Navigate using the **up arrow** and **down arrow**\n",
|
||||
"- Convert the cell type to Markdown (**m**) or code (**y**)\n",
|
||||
"- See keyboard shortcuts using **h**\n",
|
||||
"- Switch to Edit mode using **Enter**\n",
|
||||
"\n",
|
||||
"**Edit mode** (green border)\n",
|
||||
"\n",
|
||||
"- **Ctrl+Enter** to run a cell\n",
|
||||
"- Switch to Command mode using **Esc**\n",
|
||||
"\n",
|
||||
"### IPython and Markdown resources:\n",
|
||||
"\n",
|
||||
"- [nbviewer](http://nbviewer.ipython.org/): view notebooks online as static documents\n",
|
||||
"- [IPython documentation](http://ipython.org/ipython-doc/stable/index.html): focuses on the interpreter\n",
|
||||
"- [IPython Notebook tutorials](https://github.com/jupyter/notebook/blob/master/docs/source/examples/Notebook/Examples%20and%20Tutorials%20Index.ipynb): in-depth introduction\n",
|
||||
"- [GitHub's Mastering Markdown](https://guides.github.com/features/mastering-markdown/): short guide with lots of examples"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Resources for learning Python\n",
|
||||
"\n",
|
||||
"- [Codecademy's Python course](http://www.codecademy.com/en/tracks/python): browser-based, tons of exercises\n",
|
||||
"- [DataQuest](https://dataquest.io/missions): browser-based, teaches Python in the context of data science\n",
|
||||
"- [Google's Python class](https://developers.google.com/edu/python/): slightly more advanced, includes videos and downloadable exercises (with solutions)\n",
|
||||
"- [Python for Informatics](http://www.pythonlearn.com/): beginner-oriented book, includes slides and videos"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Comments or Questions?\n",
|
||||
"\n",
|
||||
"- Email: <kevin@dataschool.io>\n",
|
||||
"- Website: http://dataschool.io\n",
|
||||
"- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
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|
||||
"from IPython.core.display import HTML\n",
|
||||
"def css_styling():\n",
|
||||
" styles = open(\"styles/custom.css\", \"r\").read()\n",
|
||||
" return HTML(styles)\n",
|
||||
"css_styling()"
|
||||
],
|
||||
"language": "python",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"html": [
|
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"<style>\n",
|
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|
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" font-family: \"Computer Modern\";\n",
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|
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" div.cell{\n",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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" margin-bottom: 1em;\n",
|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
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" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
|
||||
" line-height: 145%;\n",
|
||||
" font-size: 130%;\n",
|
||||
" width: 90%;\n",
|
||||
" margin-left:auto;\n",
|
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" margin-right:auto;\n",
|
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|
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" .CodeMirror{\n",
|
||||
" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
|
||||
" }\n",
|
||||
"/* .prompt{\n",
|
||||
" display: None;\n",
|
||||
" }*/\n",
|
||||
" .text_cell_render h5 {\n",
|
||||
" font-weight: 300;\n",
|
||||
" font-size: 16pt;\n",
|
||||
" color: #4057A1;\n",
|
||||
" font-style: italic;\n",
|
||||
" margin-bottom: 0.5em;\n",
|
||||
" margin-top: 0.5em;\n",
|
||||
" display: block;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .warning{\n",
|
||||
" color: rgb( 240, 20, 20 )\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<script>\n",
|
||||
" MathJax.Hub.Config({\n",
|
||||
" TeX: {\n",
|
||||
" extensions: [\"AMSmath.js\"]\n",
|
||||
" },\n",
|
||||
" tex2jax: {\n",
|
||||
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
|
||||
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
|
||||
" },\n",
|
||||
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
|
||||
" \"HTML-CSS\": {\n",
|
||||
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
|
||||
" }\n",
|
||||
" });\n",
|
||||
"</script>"
|
||||
],
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 1,
|
||||
"text": [
|
||||
"<IPython.core.display.HTML at 0x3fe4240>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 1
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
"source": [
|
||||
"from IPython.core.display import HTML\n",
|
||||
"def css_styling():\n",
|
||||
" styles = open(\"styles/custom.css\", \"r\").read()\n",
|
||||
" return HTML(styles)\n",
|
||||
"css_styling()"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
|
|
|||
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1913
08_grid_search.ipynb
1913
08_grid_search.ipynb
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Loading…
Reference in New Issue