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 |