/2022_siop_fri_seminar

Primary LanguageJupyter NotebookMIT LicenseMIT

SIOP 2022 Machine Learning Friday Seminar

Thanks for attending this seminar! This Github contains additional resources for you along with the code from today's session.

Session Recording

https://vimeo.com/707933519/c6651aeb79

Additional Resources

Useful Packages for R

  • Tidyverse (Suite of tools for data wrangling and manipulation)
  • Tidymodels (Suite of tools for using ML models within the tidyverse)
  • Caret (ML models)
  • Psych (statistics commonly used in psychology)
  • Swirl (to learn R)

Tidymodeling

There are a ton of great resources for learning tidymodeling. I highly suggest you try out the following to get you started.

Useful Packages for Python

  • SKLearn (ML models, this package has excellent documentation as well)
  • Pandas (used for data manipulation in python)
  • numpy (package that contains mathematical functions needed for ML)

Additional Programming Languages and Analysis Tools

During the seminar, we shared coding examples and details on both R and Python. Here are some other programming languages and tools that are sometimes used for machine learning.

  • Excel
  • AnalyzeIt - Excel package turn Excel into SPSS
  • SPSS
  • SPSS Modeler
  • SAS
  • Julia
  • Apache

There are also several IDEs (Integrated Development Environments) that can be helpful for making your experience working with R or python smoother. For R, RStudio is the most common IDE. There are multiple options for Python including Pycharm, Spyder, and VScode. Also programs like Sublime and Notepad++ can also display formatted code.

Notebooks, such as Jupyter notebooks or R markdown are also helpful for displaying code and results and for sharing your results with others.

R markdown

Vendor Services

Another route that organizations sometimes take is using a vendor that offers drag and drop style machine learning services. These services often come with dashboards and common models pre-built and ready for use.

Common vendors include:

  • Microsoft Azure
  • IBM Watson
  • Amazon Web Services (AWS)

Resources for in depth self-guided study

  • Online Courses
    • Python for Data Science and Machine Learning Bootcamp
    • Swirl - R package to learn R
    • Codecademy - Introduction to Python
    • Coursera - Andrew Ng
      • AI for Everyone
      • Machine Learning
      • Deep Learning Specialization
  • Websites, podcasts, and books
    • Towards Data Science (website)
    • An Introduction to Statistical Learning with Applications in R (book)
    • Artificial Intelligence: A Modern Approach - Peter Norvig (book)
    • Super Data Science (podcast)
    • Practical AI (podcast)
    • 3Blue1Brown (youtube)
    • Two Minute Papers (youtube)