Machine Learning From Scratch

Python implementations of some of the foundational Machine Learning models and algorithms from scratch.

While some of the matrix operations that are implemented by hand (such as calculation of covariance matrix) are available in numpy I have decided to add these as well to make sure that I understand how the linear algebra is applied.

The purpose of this project is purely self-educational.

##Update:

I have forked this excellent attempt by Eric and removed the dependency of sklearn for datasets and some pandas so that the entire library can be used by anyone where sklearn and pandas is not always installed by default, such as in my office. :)

##Current implementations: ####Supervised Learning:

• Adaboost
• Decision Tree
• K Nearest Neighbors
• Linear Discriminant Analysis
• Linear Regression
• Logistic Regression
• Multi-class Linear Discriminant Analysis
• Multilayer Perceptron
• Naive Bayes
• Perceptron
• Random Forest
• Ridge Regression
• Support Vector Machine

####Unsupervised Learning:

• DBSCAN
• Gaussian Mixture Model
• K-Means
• Partitioning Around Medoids
• Principal Component Analysis