Everything from backpropagation in machine learning to linear algebra in python!
Python C++ Matlab
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data_science
dijkstra
inflix_expressions
peak_finding
topology
.gitignore
README.md
distance_orcale.py
linear_algebra.py
machine_learning.m
queries.sql
random_testing.py
reduction_k_clique_to_decision.py

README.md

Course Projects

This is a collection of projects from various classes, both online and at my University.

Using Twitter to find the happiest state

This code

  • Access the Twitter Programming Interface(API) using Python.
  • estimates the public's perception (the sentiment) of a particular term or phrase.
  • analyzes the relationship between location and mood based on a sample of twitter data.
  • The sample size is MUCH to small in order to fit within githubs suggested file size.

Machine Learning

  • machine_learning.m
  • Machine Learning
  • Stanford University
  • No Certification yet - class in progress

This code is accomplishes Vectorized backpropagation, which allows for a better trained Neural Network.

Testing Dijstra's Algorithm

I had learned this algorithm in an early class, so i jumped on the opportunity to code up a test_generator.py that produce test_files and an accompanying graph so you could get a better idea what went wrong Take a look at the Readme in the file for more!

Peak Finding Algorithm

  • peak_finding/
  • description here
  • 6006: algorithms
  • MIT
  • I completed all the lectures and I'm currently about halfway through the assignments.

MIT 6006 is a goldmine of information. Erik Demaine is my hero. This fuled my passion for algorithms. Ok enough sentiment. Peak finding is a great exercises becuase there are so many ways to do it. Try it ourself!

Linear Algebra ... with code??!?

Think back to linear Algebra... to painful? This file gives a sample of how we used python to explore orthogonalization. Later we used these concepts to shrink an image... Pretty cool stuff!

Algorithm Analysis: DFS vs Source Removal

  • topology/
  • Check infile readme for how to run the code
  • Algorithms Design and Analysis
  • Wayne State
  • Degree expected Dec, 2014

The code in directory explains which algorithm has the better running time By using Pythons Timeit module. For the final score check the Readme. I employed some rather unorthodox data structures in both algorithms. For instance, my DFS and source removal both benefit from a slight running time boost from using sets to keep track of visited nodes.

Infix expression checker

  • infix_expression/
  • Computer Programing: Data Structures in C++
  • Wayne State
  • Degree expected Dec, 2014 2014

I Implement a program for evaluating infix expressions. The program read an infix expression from a text file, checked if the parentheses in the input expression are balanced and converted the infix expression into a postfix expression, and then evaluate that postfix expression.

Comes complete with tests!

Software Testing

  • random_testing.py
  • software testing class
  • Udacity

This class covered, coverage testing, code coverage, random testing and a whole lot more. It might be the only online class focused on testing. While many of the techinques are overkill for my small projects there are definatly concepts they were definatly concepts that will be useful on future larger projects.

Reductions

  • reduction_k_clique_to_decision.py
  • Algorithms
  • Udacity

Reductions in school are usually done only theoretically. Well thats no fun! With Udacity's help I was able to code my reduction in python

SQL

  • queries.sql
  • Stanford database class
  • Coursera

This class covers relational database, normalization, triggers, xml, json and nosql. I took it along side my database class at Wayne States class which futher covered ER diagrams and schemas.

Where is the rest?

Didn't you do a lot more? Yes, I did. But for Honor Code reasons I thought it best to not leave it all public. http://help.coursera.org/customer/portal/articles/1164381-what-is-the-honor-code-