# Coursera Machine Learning In Python (Exercise 1)

< !DOCTYPE html>

ex1

I have previously done the Coursera Machine Learning exercises in Matlab. I thought, now that I am starting to get away from Matlab and use Python more, I should re-do the exercises in Python. This is exercise 1.

In [447]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy


Part 1: Create an eye matrix. While this is incredibly simple, I want to make sure that I go through each step and provide a resulting document that a novice can follow long and understand what is happening.

In [448]:
A = np.identity(5)
print A


# Scrape Keywords from Indeed.com Job Postings

## Job Posting Crawler

This is code that will pull each job posting for a specific job title in a specific location (or Nationally) and return / plot the percentage of the postings that have certain keywords. The code is set up to search for all words except stopwords, and other user-defined words (there is probably a much more efficient way of doing this, but I had no need to change this once I had the code running). This allows the user to see common technical skills, as well as common soft skills that should be included on a resume.

NOTE: I got this idea from https://jessesw.com/Data-Science-Skills/. Obviously, just using his code would be of no real benefit to me, as I wanted to use the idea to help better my skills with scraping data from HTML files. So, I used his idea and developed my own code from scratch. I also modified the overall process a bit to better fit my needs.

NOTE2: This code will not be able to identify multiple-word skills. So, for example, ‘machine learning’ will show up as either ‘machine’ or ‘learning’. However, ‘machine’ could show up for other phrases than ‘machine learning’.

To run the code, change the city, state, and job title to whichever you wish. After generating the plot, you might need to add ‘keywords’ to the attitional_stop_words list if you do not want them to be included.
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