Machine Learning #
stateDiagram-v2
classDef ahsState font-style:italic,font-weight:bold,fill:lightblue
State1: Machine Learning
State1 --> SL:::ahsState
SL: Supervised Learning
SL --> Classification
Classification --> NB
NB: Naive Bayes
NB --> NN
NN: Nearest Neighbour
NN --> SVM
SL --> Regression
Regression --> LR
LR: Linear Regression
LR --> NNetwork
NNetwork: Neural Network
NNetwork --> DT
DT: Decision Tree
State1 --> USL:::ahsState
USL: Unsupervised Learning
USL --> Clustering
Clustering --> KM
KM: K-Means
KM --> GM
GM : Gaussian Matrix
note right of GM
Neural Networks
end note
GM --> HM
HM : Hidden Markov
State1 --> Reinforcement:::ahsState
Reinforcement --> DM
DM: Decision Making
Supervised Learning uses labeled data and output training data.
Unsupervised Learning learns relationship and patterns from unlabelled raw data.
Reinforcement uses rewards and punishment model to train. The agent learns a policy/strategy that maximises its rewards over time.