"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 31, 2016

Day #29 - Decision Trees

  • Hierarchical, Divide and Conquer strategy, Supervised algorithm
  • Works on numerical data
  • Concepts discussed - Information gain, entropy computation (Shanon entropy)
  • Pruning based on chi-square / Shannon entropy
  • Convert all string / character into categorical / numerical mappings
  • You can also bucketize continuous variables
Basic Python pointers

Good Reads
Link1 , Link2, Link3, Link4, Link5, Link6

Happy Learning!!!

August 15, 2016

Day #28 - R - Forecast Library Examples

Following Examples discussed. Library used - R - Forecast Library
  • Moving Average
  • Single Exponential Smoothing - Uses single smoothing factor
  • Double Exponential Smoothing - Uses two constants and is better at handling trends
  • Triple Exponential Smoothing - Smoothing factor, trend, seasonal factors considered
  • ARIMA

Happy Learning!!!

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!!!