REFERENCES.md

References for the implementations

This list will be updated.

Supervised Learning

• Decision Tree

  1. Fastai ML Course Lesson 5

  2. Fastai ML Course Lesson 7

• Elastic Net

  1. Wikipedia Explanation
  2. Datacamp Tutorial

• Gradient Boosting Machine

• K-Nearest Neighbor

  1. Stanford CS231N Course by Andrej Karpathy (2016)

• Lasso Regression

  1. Wikipedia Explanation
  2. Datacamp Tutorial

• Linear Regression

  1. Coursera ML Course (Legendary)

• Logistic Regression

  1. Coursera ML Course (Legendary)

• Naive Bayes

  1. Fastai NLP Course

• Neural Networks

  1. deeplearning.ai Deep Learning Specialization Course 1
  2. deeplearning.ai Deep Learning Specialization Course 2
  3. Stanford CS231N Course by Andrej Karpathy (2016)

• Perceptron

  1. Stanford CS231N Course by Andrej Karpathy (2016)

• Polynomial Regression

• Random Forest

  1. Fastai ML Course Lesson 5

  2. Fastai ML Course Lesson 7

• Ridge Regression

  1. Wikipedia Explanation
  2. Datacamp Tutorial

Unsupervised Learning

• DBSCAN

  1. Wikipedia Explanation with Pseudocode

• K-Means

• PCA