Please find below a list of tutorials which cover topics on data science areas, as well as other useful resources including technical background and coding approaches.

## 01: Machine Learning - Background

Entry | Link | Description | |
---|---|---|---|

1 | What is Machine Learning? | Background to Machine Learning | |

2 | Supervised background | An overview of Supervised Learning | |

3 | Unsupervised background | An overview of Unsupervised Learning | |

4 | Comparison of learning methods | A brief comparison between supervised vs unsupervised vs semi-supervised vs reinforced techniques | |

5 | Real world applications | Examples of where machine learning is used in the real world | |

6 | Actuarial applications | Examples of current and potential use in the actuarial industry |

## 02: Overview of Supervised Approaches

## 03: Overview of Unsupervised Approaches

Entry | Link | Description | |
---|---|---|---|

1 | Clustering | Overview of clustering techniques and applications | |

2 | Association rules | Overview of association rules techniques and applications | |

3 | Dimensionality reduction | Overview of dimensionality reduction techniques and applications |

## 04: Overview of Reinforcement Learning Approaches

## 05: Neural networks

Entry | Link | Description | |
---|---|---|---|

1 | Neural networks | Introduction to neural networks |

## 06: Mathematical background

Entry | Link | Description | |
---|---|---|---|

1 | Linear algebra | Overview of Linear Algebra |

## 07: Coding

Entry | Link | Description | |
---|---|---|---|

1 | Python & Python-based libraries | Introduction to coding in Python and other useful libraries e.g. Pandas, Numpy | |

2 | ML frameworks | Introduction to Tensorflow, Keras, & PyTorch | |

3 | SciKit | Introduction to SciKit library |