Overview of machine learning algorithms. Infer/predict -> error/loss -> train/learn. Supervised, unsupervised, reinforcement learning.
## Resources
- Tour of Machine Learning Algorithms (http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms) `article:easy`
- The Master Algorithm (http://amzn.to/2kLOQjW) `audio:medium` Semi-technical overview of ML basics & main algorithms
## Episode
Learning (ML)
- 3-step process
** Infer / Predict
** Error / Loss
** Train / Learn
- First as batch from spreadsheet, then "online" going forward
** Pre-train your "model"
** "Examples"
** "Weights"
- Housing cost example
** "Features"
** Infer cost based on num_rooms, sq_foot, etc
** Error / Loss function
Categories
- Supervised learning
** Vision (CNN)
** Speech (RNN)
- Unsupervised
** Market segmentation
- Reinforcement & Semi-Supervised
** Planning (DQN): Games (chess, Mario); Robot movement
