Machine Learning

TOC

1. Data Processing
2. Models
3. Contribute
4. Reference

TLDR

The purpose of this repo is to compile (no pun intended) a knowledge base, and reference repo, for all things machine learning. The information present is not mine, and originates from my graduate work in machine learning, online classes I have completed, or are currently taking, as-well-as articles and blog post I find interesting. All attribution and citations are in the footnote where possible, with a more detailed reference in the reference section at the end.

Data processing

1. Getting the data
2. Importing the libraries
   1. import matplotlib.pyplot as plt
      1. matplotlib is the library and pyplot is the sub-library
3. Importing the dataset
4. Missing data
5. Categorical data
6. Splitting the dataset into training and test sets
7. Feature scaling
8. Data preprocessing template

Models

1. Naive Bayes Classifier ¹

Contribute

Reference

1. Datasets

   1. Machine Learning A-Z

2. Anaconda with Python3.6 for specific OS:

   1. Windows
   2. Windows 64 bit
   3. Mac
   4. Linux
   5. Linux 64 bit

3. R and RStudio for specific OS:

   1. R
   2. R-Studio

1: ODU MISM607, Machine Learning I, Machine Learning A-Z, Hands on Python & R in Data Science (Udemy 2018)