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 :computer: What is Machine Learning? Background to Machine Learning
2 :computer: Supervised background An overview of Supervised Learning
3 :computer: Unsupervised background An overview of Unsupervised Learning
4 :computer: Comparison of learning methods A brief comparison between supervised vs unsupervised vs semi-supervised vs reinforced techniques
5 :computer: Real world applications Examples of where machine learning is used in the real world
6 :computer: 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 :bar_chart: Clustering Overview of clustering techniques and applications
2 :bar_chart: Association rules Overview of association rules techniques and applications
3 :bar_chart: Dimensionality reduction Overview of dimensionality reduction techniques and applications

04: Overview of Reinforcement Learning Approaches

05: Neural networks

Entry   Link Description
1 :fireworks: Neural networks Introduction to neural networks

06: Mathematical background

Entry   Link Description
1 :microscope: Linear algebra Overview of Linear Algebra

07: Coding

Entry   Link Description
1 :milky_way: Python & Python-based libraries Introduction to coding in Python and other useful libraries e.g. Pandas, Numpy
2 :milky_way: ML frameworks Introduction to Tensorflow, Keras, & PyTorch
3 :milky_way: SciKit Introduction to SciKit library