/workshop-intro-ml

[Workshop] A workshop I created to teach Machine Learning.

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Intro to ML (Crash Course) → JEST internal workshop

Workshop to introduce Machine Learning concepts.

Requirements

  • Have a Google account (1) or install Jupyter Notebook environment (2)

Installation

Option 1: Go to the link and make a copy.

Option 2 (recommended):

  1. Install Jupyter notebook
  2. Install Git
  3. git clone https://github.com/tamagusko/workshop-intro-ml.git
  4. cd workshop-intro-ml
  5. pip install -r requirements.txt

Deploy slides

Use quarto: quarto preview slides.qmd

Topics

  • A Data Science project
  • Data Collection
  • Exploratory data analysis
    • Data visualization
  • Preprocessing
    • Split data in train/test
  • Models
    • Decision Tree
    • Random Forest
    • KNN (k-nearest neighbors)
    • XGBoost (eXtreme Gradient Boosting)
    • Neural Network (Multilayer perceptron)
  • Comparison of models (metrics)
  • Improving the model
  • Saving and loading models
  • Overfitting
  • Automating tasks (Choosing the best model)

Complementary material

  • [1] PEP 8 - The Style Guide for Python Code.
  • [2] Clean Code Python - Quick guide with good practices adapted from Clean Code to use in Python.
  • [3] Cheat Sheets - Collecting Data Science Cheat Sheets.
  • [4] Machine Learning Bites - An interview guide on common Machine Learning concepts, best practices, definitions, and theory.

Please direct bug reports and pull requests to the GitHub page. To contact me directly, send email to tamagusko@gmail.com.

-- Tiago

Copyright (c) 2022, Tiago Tamagusko.