"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

August 08, 2016

Applied Machine Learning Notes

Supervised Learning
  • Classification (Discrete Labels)
  • Regression (Output is continuous, Example - Age, Stock prices)
  • Past data + Past Outputs used
Unsupervised Learning
  • Dimensionality reduction (Data in higher dimensions, Remove dimension without losing lot of information)
  • Reducing dimensionality makes it easy for computation (Continuous values)
  • Clustering (Discrete labels)
  • No Past outputs, Only current data
Reinforcement Learning
  • All Game Playing is unsupervised
  • Learning Policy
  • Negative / Positive reward for each step
Type of Models
  • Inductive (Learn model, Learn from a function) vs Transductive (Lazy learning ex- Opinion from like minded people)
  • Online (Learn from every new incoming tweet) vs Offline (Look past 1 Yeat tweet)
  • Generative (Apply Gaussian on Data, Use ML and compute Mean / Variance) vs Discriminative (Two sides of Line)
  • Parametric vs Non-Parametric Models
Happy Learning!!!

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