Machine Learning (ML)

  • ML Fundamentals

    Basic knowledge of Machine Learning.

  • Model Selection

    How to properly select a Machine Learning model.

  • Regression

    Describe the relationship between one or more independent variables and a response, dependent, or target variable.

  • Classification

    Assign a class label to imput sample.

  • Decision Trees

    A non-parametric supervised learning method used for classification and regression.

  • Ensemble Learning

    Use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.

  • Non-parametric

    Do not make strong assumptions about the form of the mapping function or data distribution.

  • Unsupervised Learning

    Learn patterns from untagged data.