Machine Learning Algorithms

This repository intends to host some codes for my studies in Machine Learning (ML) algorithms. It is my guide to learning details about ML by implementing them. Here you can find out how to load data, validate models, and an implementation of a suite for step-by-step sample code. Learning while coding is my goal with this repository.

Disclaimer: These codes are experimental and poorly tested. I cannot guarantee that results are accurate and suitable for replication in research purposes or professional applications.

Authorship: André Luis A. Reis

E-mails: reisandreluis@gmail.com

Topics

• Data preparation
• Linear algorithms
• Nonlinear algorithms
• Ensemble algorithms

What you are going to find here

• Data preparation

  • Scale datascale_data.ipynb
  • Evaluation methods evaluation_methods.ipynb
  • Evaluation metrics evaluation_metrics.ipynb
  • Baseline models baseline_models.ipynb

• Linear algorithms

  • Harnesses test harnesses_test.ipynb
  • Simple linear regression simple_regression.ipynb
  • Multivariate regression multivariate_regression.ipynb
  • Logistic regression logistic_regression.ipynb
  • Perceptron perceptron.ipynb

• Nonlinear algorithms

  • Classification and Regression trees classification_regression.ipynb
  • Naive Bayes naive_bayes.ipynb
  • k-Nearest neighbors k_nearest.ipynb
  • Learning vector quantization vector_quantization.ipynb
  • Backpropagation backpropagation.ipynb

• Ensemble algorithms

  • Bootstrap aggregation bootstrap_aggregation.ipynb
  • Random forest random_forest.ipynb
  • Stacked generalization stacked_generalization.ipynb

References

• Brownlee, J. 2019. Machine Learning Algorithms From Scratch, Machine Learning Mastery.

License

"Machine Learning"

by André L A Reis is licensed under a Creative Commons Attribution 4.0 International License.