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#!/usr/bin/python

#
# LICENSE: MIT
#
# Copyright (C) 2014 Samuel Stauffer
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation

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# List unique values in a DataFrame column
# h/t @makmanalp for the updated syntax!
df['Column Name'].unique()

# Convert Series datatype to numeric (will error if column has non-numeric values)
# h/t @makmanalp
pd.to_numeric(df['Column Name'])

# Convert Series datatype to numeric, changing non-numeric values to NaN
# h/t @makmanalp for the updated syntax!

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def normalize(df):
  """
  Function for min-max Scaling a pandas DataFrame
  @param:
  Takes a pandas DataFrame: df
  Returns: a normalized DataFrame
  along with a dict containing rescaling 
  coef which can be used in below function.
  """
    result = df.copy()

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Scikit Learn

• supports numpy array, scipy sparse matrix, pandas dataframe.
• Estimator - learns from data: can be a classification, regression , clustering that extracts/filters useful features from raw data - implements set_params, fit(X,y), predict(T) , score (judge the quality of fit / predict), predict_proba (confidence level)
• Transformer - transform (reduce dimensionality)/ inverse_transform, - clean (sklearn.preprocessing), reduce dimensions (sklearn.unsupervised _reduction), expand (sklearn.kernel_approximation) or generate feature representations (sklearn.feature_extraction).

sklearn.cluster

properties: labels_, cluster_centers_. distance metrics - maximize distance between samples in different classes, and minimizes it within each class: Euclidean distance (l2), Manhattan distance (l1) - good for sparse features, cosine distance - invariant to global scalings, or any precomputed affinity matrix.

• dbscan - deterministicly separate areas of high density from

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basic Machine Learning pre-requisites:

• python http://docs.python-guide.org/en/latest/intro/learning/
• Jupyter http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/
• Scipy http://www.scipy-lectures.org/
• Pandas https://pandas.pydata.org/pandas-docs/stable/10min.html
• Scikit-Learn http://scikit-learn.org/stable/user_guide.html
• Seaborn charts https://seaborn.pydata.org/tutorial.html
• brief intro to Neural nets https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
• Spark MLLib https://spark.apache.org/mllib/
• Mathematical Foundations of Machine Learning: https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA

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• Linear Regression
  • A BEGINNERS GUIDE TO REGRESSION TECHNIQUES
  • Linear Regression Algorithm | Linear Regression in Python | Machine Learning Algorithm | Edureka - YouTube
  • In Depth: Linear Regression | Python Data Science Handbook
  • Linear Models - YouTube
  • Statistics 101: Linear Regression, The Very Basics - YouTube
  • How to Implement Linear Regression From Scratch in Python
• [Linear Regression using Python - Towards Data Science](https://towardsdatascience.com/linear-regression-us

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This gist contains out.tex, a tex file that adds a PDF outline ("bookmarks") to the freely available pdf file of the book

An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

http://www-bcf.usc.edu/~gareth/ISL/index.html

The bookmarks allow to navigate the contents of the book while reading it on a screen.