Automated Machine Learning

This repository holds the slides and examples of a class on Automated Machine Learning as Jupyter notebooks.

TOC

1 Introduction

1.1 What is Automated Machine Learning?

1.2 Machine Learning 101

1.3 Model Selection and Assessment

1.4 Hyper-parameter Tuning

II Tools & Techniques for Automated Machine Learning

2.1 Machine Learning Pipelines

2.2 Bayesian Hyper-parameter Optimization

2.3 Pipeline Optimization

2.4 Advanced Topics

III Create Value using Automation

3.1 Democratizing Machine Learning

3.2 Model Factories

3.3 Continous Learning

Requirements

Python

Create a new conda env:

conda create -n aml python=3.7
source activate aml

Jupyter

conda install -y -c conda-forge jupyterlab
conda install -y -c conda-forge ipywidgets

Pydata & dependencies

conda install -y -c conda-forge matplotlib
conda install -y -c conda-forge seaborn

conda install -c anaconda scikit-learn==0.21.2

pip install git+https://github.com/kitchoi/scikit-optimize@fix-762-sklearn-0.21
pip install tpot==0.10.2

Optional

conda install -y --freeze-installed -c conda-forge graphviz
conda install -y --freeze-installed -c conda-forge python-graphviz