How to deploy applications with Elastic Beanstalk

Easy to begin, impossible to outgrow.

1. Knowledge
1. Elastic Beanstalk is a Amazon Web Service for deploying and hosting software applications.
2. If this sounds like Heroku, your intuition is correct.
2. Strategy
1. Here's a tutorial specific for deploying a Django application.
2. Setup a python environment with Django.
3. Create a Django project.
4. Configure the app for Elastic Beanstalk.
5. Deploy with the EB CLI.
3. Execution
1. Common issues:
1. Powershell ExecutionPolicy
2. EB CLI Could not find any platforms
3. Server 404 response after deploy

How to teach a machine to learn

Machine learning in Python.

1. Knowledge
1. Scikit-learn is a toolkit for machine learning in Python.
2. It explains how it solves machine learning problems. 
2. Strategy
1. Scikit-learn provides a tutorial for getting started.
2. Although, where do we even start if our application is huge, nuanced and complex? Let's use nearest-neighbor thinking to build off a similar solution! (a quick google search found it)
3. Execution

Installing IDLE in Linux Mint

Within Terminal enter:

sudo apt-get install idle

Parsing in Python

Let's make sense of this pile of data.

Reading and parsing data are the first two building blocks of a larger process, compiling, so it's always useful to know a couple of good methods.

A quick Google search of "python reading data" got me to http://docs.python.org/2/tutorial/inputoutput.html, for some simple reference material.

Now, testing is always a fun way to find out what's going on behind the code, so let's see if we can just pull out a bit of the important data here so we don't overwhelm ourselves.

Now, continuing from before, lets convert the data object to a useful string/array.

>> theData = str(html.read()).split(',')

Now let's see what this looks like.

>> for i in range(5):

         print(i, theData[i])

This smaller data structure will come in handy when setting up our larger program design.

Here's the result:

0 b'{"Key":"100-og"

1 "Name":"$100 OG"

2 "Category":"Hybrid"

3 "Symbol":"100"

4 "Abstract":"$100 OG is a hybrid cannabis strain that originally became popular in Southern California. It produces large flowers and a potent high."

Now, wait, your probably thinking, what's that b'{ doing there?

This data is meant to be parsed with the use of json modules, not bare bones python split statements, and that should massively simplify the process.

Although; you may have to redesign your parsing algorithm to completely remove all persisting characters and strings.

Reading API Data

In Python, this is as easy as running a couple of commands in IDLE.

from urllib import urlopen

html = urlopen("http://www.leafly.com/api/details/100-og")

html.read() 

HTTPResponse instances also have some more methods and attributes, which can be found here: http://docs.python.org/release/2.2.3/lib/httpresponse-objects.html

This can be additionally simplified by importing and using the json library.