💻 my own machine learning library with algorithms implemented from scratch
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data
decision_tree
feature_transform
k-means-clustering
kNN
kernels
logistic_binary
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perceptron
regression
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README.md
backprop-exeriment-2.py
backprop-experiment.py
bp-exp-2
bp-exp-2.py

README.md

Machine Learning Algorithms

A personal machine learning library in Python Anything involving deep learning is over at https://github.com/rohan-varma/neuralnets.

Algorithms with linear decision boundaries (perceptrons, logistic regression, linear regression) are in the linear_algorithms folder, algorithms with non-linear decision boundaries (k-nearest neighbors, decision trees) are at the nonlinear_algorithms directory.

Currently Implemented:

  • Basic sampling from distributions and plotting them on matplotlib
  • Perceptron algorithm
  • Voting perceptron
  • K-Fold Cross validation to detect overfitting
  • Hyperparameter tuning with K-Fold CV, and other stuff
  • Logistic Regression
  • Linear Regression

Todo:

  • Support for feature transformations
  • SVMs
  • Decision Trees
  • Kernel functions
  • Multiclass logistic regression
  • Multiclass SVMs
  • k nearest neighbors
  • ridge regression
  • lasso regression
  • kernelized regression
  • kernelized nearest neighbors
  • kernelized SVM