- Basic Statistics
- Python coding
- Feature versus Target
- Features are data attributes, variables, we use for training in order to predict the target result.
- Training versus testing
- Split data into training and testing subsets and shuffle
- Train test split
- Data Split
- Performance Metric
- Coefficient of Determination R
- Decision Tree and maximum depth
- Not covered in details online
- We had a separate lecture in-person on decision tree
- Entropy - coin example
- Coin is random, 50% head 50% tail, can't predict it Entropy = 1 bit
- Coin with double heads, 100% head, can predict always get head, no Entropy. Entropy = 0
- Learning Curve
- Model Selection
- occam's razor the simpler model is preferred [wikipedia source https://en.wikipedia.org/wiki/Occam%27s_razor]
Sunday, October 30, 2016
Udacity Machine Learning Nanodegree Udacity Connect Intensive Cheatsheet Key Concepts
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