Showing posts from March, 2018

Using Python for Research

EdX HarvardX Over the past two weeks I've started an online course covering python basics, python research libraries, and some specific case studies. To start, is a fantastic site with a great cause: spreading knowledge. You can take free courses from leading institutions like Harvard , but enough of the unpaid promotion. My interest in this course stemmed from a few places. Namely, the desire to learn python, use machine learning libraries, and general lust for information. EdX has a ton of learning python and python for _blank_ courses available from universities & corporations. However, as a totally bias human being, I wanted to take the Harvard   course being I have a sibling there (or maybe I don't, you'll never know internet reader). Regardless, I wanted to learn the programming language python in a directly applicable way and this course offered that. So I signed up. Python appealed to me after watching videos on machine learning and seeing how easy pyt

Learning Python

robobcb$ python3;  After watching a youtube video on machine learning by  Siraj Raval, I was inspired. Seeing how 10 lines of python could utilize a machine learning library was enough to convince  me that the C and JAVA I learned in uni were not enough. Time to learn another language. In this video "Intro to Deep Learning #1" Siraj lays out his code for utilizing the tree classifier from scikit learn to predict the gender of someone given the height, weight, and shoe size. Whether or not these actually correlate  to a gender is irrelevant as this is about how different algorithms learn from a given data set.  In the video, I was challenged to take the code he started and move it to the next level by adding in 3 more classifiers and then compare their accuracies. If implemented correctly, the code should only print the most accurate prediction and which classifier predicted it. I used three libraries to complete this challenge: numpy, sklearn, and text