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
• clf.fit( ... ) #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