Elvis Saravia omarsar

Resources for Machine Learning in Ruby

Gems

liblinear-ruby: Ruby interface to LIBLINEAR using SWIG
classifier-reborn: Bayesian and LSI classification
dependencies: GSL

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

Kibana Cheat Sheet

Some helpful commands for Kibana using the Dev Tools feature:

GET _all

GET twitter/tweet/_count

GET my_index

Computing Resources

Operating system: Preferably Linux or MacOS. If you have Windows, things may crash unexpectedly (try installing a virtual machine if you need to)
RAM: Minimum 8GB
Disk space: Mininium 8GB

Software Requirements

Here is a list of the required programs and libraries necessary for this lab session. (Please install them before coming to our lab session on Tuesday; this will save us a lot of time, plus these are the same libraries you may need for your first assignment).

Python 3+ (Note: lab and assignment will be done strictly using Python 3)
Install latest version of Python 3

These are the instrucitons to submit assignment of Data Mining 2017 Fall:

Assuming you have cloned the data_mining_hw_1.git repository to your account, you first clone your fork to start making necessary changes, by:

git clone https://github.com/YOURUSERNAME/data_mining_1.git

It's a good practise that when you fork a repo, you fetch from upstream and create a new branch. In this assignment you don't need to fetch changes since the homework repo is finalized. But you are recommended to create a new branch instead of working directly from the master branch. (If you have already started working on master, it is fine either way.) Create branch and give it a meaningful name:

git branch newbranch

	// Word cloud layout by Jason Davies, http://www.jasondavies.com/word-cloud/
	// Algorithm due to Jonathan Feinberg, http://static.mrfeinberg.com/bv_ch03.pdf
	(function(exports) {
	function cloud() {
	var size = [256, 256],
	text = cloudText,
	font = cloudFont,
	fontSize = cloudFontSize,
	rotate = cloudRotate,
	padding = cloudPadding,

	<html>
	<head>
	<script src="https://www.gstatic.com/firebasejs/3.0.0/firebase.js"></script>
	<title>ZeroToApp</title>
	<style>
	#messages { width: 40em; border: 1px solid grey; min-height: 20em; }
	#messages img { max-width: 240px; max-height: 160px; display: block; }
	#header { position: fixed; top: 0; background-color: white; }
	.push { margin-bottom: 2em; }
	@keyframes yellow-fade { 0% {background: #f2f2b8;} 100% {background: none;} }

	from os import path
	from PIL import Image
	import numpy as np
	import matplotlib.pyplot as plt
	from wordcloud import WordCloud, STOPWORDS

	def google_authenticate():
	# Authenticate first so the Google Drive library can detect your credentials.
	from google.colab import auth
	auth.authenticate_user()

	from googleapiclient.discovery import build
	drive_service = build('drive', 'v3')
	return drive_service