Overview of supervised learning
Regression and classification
Regularization methods
Trees
Bagging and boosting
- Random Forests
- (Deep) neural networks
-Feed forward
-Recurrent neural networks
Unsupervised learning
Clustering algorithms
Dimensional reduction
Anomaly detection
- Autoencoders
- LSTM
Specializations
Reinforcement learning
Bayesian neural networks
Model stacking & ensemble models
Hyperparameter tuning
