An introduction to Machine Learning

5 days

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

Interested in a demo?

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