ML_with_Python

A repository for code and notes created as I study an Introduction to Machine Learning with Python (from O'Reilly by Muller and Guido)

Useful websites

• http://web.stanford.edu/~hastie/ElemStatLearn/
• http://scikit-learn.org/
• http://bit.ly/advanced_machine_learning_scikit-learn
• https://github.com/amueller/introduction_to_ml_with_python
• http://scikit-learn.org/stable/documentation
• http://scikit-learn.org/stable/user_guide.html

Folder for course on my local machine:

/Users/alexanderambrioso/Documents/GitHub/ML_with_Python

Necessary imports

• import numpy as np
• import matplotlib.pyplot as plt
• import pandas as pd
• import mglearn
• from iPython.display import display'

Chapter 1

Introduction (Completed 6/10)

Highlights

• Introduction to Python
• List of libraries that need to be imported to complete all examples in the book.
• Definitions of supervised and unsupervised learning
• Explanation of the k-nearest Neighbor algortithm for ML (kNN)
• A nice example for identifying Iris species using supervised learning and a kNN

Ideas

• Write a function or class for performing the kNN learning of this example:
  def kNN(data, target): pass
• Create an identifier for digits based on asterisk patterns that look like digits
• Create an identifier for digit based on the MNIST database.

Chapter 2

[Chapter 7: Working with Text Data]