BDA-602 - Machine Learning Engineering

Dr. Julien Pierret

Lecture 2

Python Libraries - Demo

  • Setup pip
  • Make a PR

scipy

In Summary (1 of 2)

  • We will use pip-compile along with requirements.in for dependency management
  • argparse will help us pass arguments to our progrems
  • Pandas
    • Load small datasets
    • Preparing datasets for analysis
  • Numpy
    • Manipulate arrays
    • Built new features
  • sci-kit learn
    • Build repeatable transformations
    • Train ML models
    • Build reusable pipelines

›

In Summary (2 of 2)

  • Plotly
    • Inspect candidate predictors
    • Visualize our data / results
  • Difference with mean of response
    • Our "go-to" for visualizing relationships
  • Jupyter Notebooks
    • Are garbage 💩🚽, 🗑️

›

Homework - Tutorials 📓

Homework - References 📚

Homework - Cheatsheets