Machine Learning Zero-To-Hero

About

Python implementations from scratch of some of the fundamental Machine Learning models and algorithms. The purpose of this project is to present the inner workings of the algorithms in a transparent way.

Table of contents

• Machine Learning from Scratch
  • About
  • Table of Contents
  • Installation
  • Implementations
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
    • Deep Learning
  • Examples

Installation

Implementations

Supervised Learning

• Linear models:

  • Ordinary least squares
  • Ridge regression
  • Lasso
  • Elastic Net
  • Least Angle Regression
  • LARS Lasso
  • Orthogonal Matching Pursuit (OMP)
  • Bayesian regression
  • Softmax regression
  • Ridge Classifier
  • Generalized linear models
  • Stochastic gradient descent
  • Perceptron
  • Passive aggressive algorithms
  • Robustness regression
  • Quantile regression
  • Polynomial regression

• Linear and Quadratic Discriminant Analysis

• Kernel Ridge Regression

• Support Vector Manchines

• Stochastic Gradient Descent

• Nearest Neighbours

• Gaussian Processes

• Cross Decomposition

• Naive Bayes

• Decision Trees

Unsupervised Learning

Reinforcement Learning

Deep Learning

• Neural Network

• Architectures:

  • ResNet
  • GoogLeNet_V1

Examples