- 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
Quickly check Tensorflow version number and successful installation.
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...
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...
Dilys Sun Got a question about web development dev bootcamps? Ask them here or @i_stanford Your question shall be answered by myself, othe...