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Provide a brief introduction to this document, the goals, what this isn't, and the process used by the focus group to develop this document.

The Hands-on Machine Learning Study Materials for Research Software Engineers Focus Group was formed to share study materials and other related resources that will be useful for interested Research Software Engineers at different skill levels in Machine Learning (ML) expertise to learn ML skills. These are some of the goals of this focus group:

1. Come up with a set of good hands-on study materials that Research Software Engineers can use to develop and/or improve Machine Learning (ML) skills
2. These materials should include ones that are useful for beginners and people with intermediate skills in ML
3. Gather documentation on ML models that generally work for different problem areas or are based on some parameters (e.g., amount of training data for supervised learning)

4. Collate and adapt the collected materials if possible

5. Document the collected materials / URLs and categorize them (based on the focus groups' criteria)
6. Choose different areas within ML to focus on:
   1. Traditional Machine Learning
   2. Deep Learning - Text Analysis
   3. ML Operations and relevant services
7. Write some code examples that can be shared (e.g. Jupyter Notebooks)
8. Collect documentation on existing NCSA hardware for ML (HAL, Delta)

This working document is the Focus Group's report and this document contains the study materials and other learning resources collected by the Focus Group members organized into different sessions and subsections. This document is not an extensive survey of the available study materials and we do not claim that this document list all the available study materials or resources. The Focus Group met every two weeks and discussed the materials collected till then and started documenting these here. 

Traditional Machine Learning (Minu Mathew,  Sandeep Puthanveetil Satheesan)

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