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` - Machine Learning with R (http://amzn.to/2n5fSUF) `book:medium` - 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