aiacademy: 機器學習  實作: ch09 (unsupervised learning)
Principlal Component analysis (PCA)
from sklean.decomposition import PCA
tSEN
Kmeans
Machine Learning  Summary
What we have learned

We mostly discussed supervised learning
 Linear regression
 Logistic regression
 SVM
 KNN
 Decision Trees
 random forest
 AdaBoost
 Gradient Boosting
 etc.

We also discussed a few unsupervised learning methods
 PCA
 tSNE
 Kmeans
 hierachical clustering
What was not covered (but probably important if you want to dive deeper in AI/ML)
 Deep Learning
 Reinforcement learning
 Bayesian learning
 Numerical optimization
 Searching
 Knowledge representataion