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!!!

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!!!

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!!!