Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees
## Resources
- Andrew Ng Week 8 (https://www.coursera.org/learn/machine-learning/resources/kGWsY)
- Tour of Machine Learning Algorithms (http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms) `article:easy`
- Elements of Statistical Learning (http://amzn.to/2tWW8He) `book:hard`
- Pattern Recognition and Machine Learning (http://amzn.to/2sDIIfb) (Free PDF? (https://goo.gl/aX038j)) `book:hard`
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` (replaced R book)
- Which algo to use?
** Pros/cons table for algos (https://blog.recast.ai/machine-learning-algorithms/2/) `picture`
** Decision tree of algos (http://scikit-learn.org/stable/tutorial/machine_learning_map/) `picture`
## Episode
KNN (supervised)
Unsupervised
- Clustering -> K-Means
- Association rule learning / Market basket -> Apriori
- Dimensionality Reduction -> PCA
Decision Trees (supervised, classify/regress)
- Random Forests
- Gradient Boost
