Skip to content

Latest commit

 

History

History
75 lines (54 loc) · 2.73 KB

README.md

File metadata and controls

75 lines (54 loc) · 2.73 KB
Raw

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)