Tuesday, March 21, 2017

Udacity Machine Learning Nanodegree - Projects Step by Step Walkthrough High Level Cheat Sheet

High level steps to solve Udacity Machine Learning Nanodegree projects:

  • Import dependencies: numpy, pandas, sklearn, matplotlib
  • Data cleaning:
    • Replace all data with numeric value such as binaries 0 and 1 or scale down to between -1 to 1, or 0 to 1 (normalization). 
    • Replace yes/no binary answers with 1,0
    • Replace categorical data A, B, C with dummy columns |A|B|C| use 1 if true, 0 if false
  • Split data into features and target aka label
  • Perform initial exploration, turns data CSV into Pandas.DataFrame
    • Computer summary stats: mean, counts etc.
  • from sklearn import model
  • clf = sklearnmodel.model() #specify the classifier
  • ... ) #fit the model wither parameters
  • clf.predict() #make predictions
  • Metrics:
    • R^2 R squared - great for linear regression 0 to 1, 1 being the best
  • Errors:
  • This list is under construction

Sklearn machine learning model cheat sheet
What are the best algorithms to use for each machine learning problem?
Classification versus regression
Supervised versus unsupervised

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