# Introduction

# Welcome to MILO-ML's Auto-ML platform

MILO-ML is an automated machine learning (Auto-ML) platform which aims to provide an easy-to-use interface powered by an efficient search engine that can find the best machine learning model for a given dataset. In summary:

  • No machine learning experience is needed
  • No software engineering expertise is needed
  • No programming is required

This step-by-step guide will enable you to use your own datasets in creating predictive machine learning (ML) models through MILO-ML's simple yet very powerful and fully automated binary classification Auto-ML platform. MILO-ML’s Auto-ML platform, if you choose, will also allow you to easily deploy your new ML models (go live) for making predictions on new/future data or for testing additional datasets on the ML models that have been created.

Before starting this tutorial, we recommend the following steps that will hopefully enhance your overall MILO-ML experience.

1st: We recommend reading the following two articles before getting started within MILO-ML. The first is a supervised machine learning review article and the second (published in Nature’s Scientific Reports) highlights the use of MILO-ML as an example for building ML models that can serve a particular need (e.g., help with Sepsis prediction). The review article not only gives one a more detailed look of the various supervised machine learning methods employed within MILO-ML, but it also brings some insight into ML best practices and study design. You can access these articles directly through the following links (see below):

https://journals.sagepub.com/doi/full/10.1177/2374289519873088 (opens new window)

https://www.nature.com/articles/s41598-020-69433-w (opens new window)

2nd: Sample datasets (Training and Generalization testing datasets) of a publicly available breast cancer study (obtained through the UCI’s public domain datasets) is provided here for your convenience which can be downloaded directly from this guide’s Sample Datasets section. When ready, please use this dataset to perform a test run within MILO-ML as shown in this step-by-step guide. Note: when ready to build models with MILO-ML on these sample datasets, we recommend starting with a smaller run with just Logistic regression and Support vector machine algorithms selected within MILO-ML so that the build phase can be completed in a much faster pace. This way you can get introduced to the various MILO-ML capabilities in a much more efficient manner and without a hiccup as you go through this guide while getting to know the various analytics tools that MILO-ML can offer during and after each run.

3rd: To better understand the various terms used within the MILO-ML platform, a separate "Glossary of ML MILO-ML terms" is also provided for your convenience (please see "Glossary" section for more details).