- 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
What is a domain name system (DNS)? How stuff works explains it in a very good graph I was very confused by the Wikipedia explanatio...
The bogus request from P2PU to hunt for HTML tags in real life has yielded a lot of good thoughts. My first impression was that this is stup...
I recently submitted Project 1 of the Udacity Full Stack Nanodegree curriculum. This blog post is my reflection and review on the experience...