Pharo-AI Wiki

This is the Pharo-AI Wiki. The goal of this wiki is to provide documentation and tutorials to help people start using our Pharo AI/Machine-Learning libraries.

• Getting Started page.
• Contributing guide

If you want to see other Machine Learning projects in Pharo, please see: https://github.com/pharo-ai/awesome-pharo-ml

Keep in mind that the wiki and pharo-ai is right now under construction version so not all the algorithms will be documented or with all the functionalities that we would like to have. Nevertheless, all the things that are documented here had been revised and are working.

Contents

• Tutorials
  • Linear Regression
  • Logistic Regression
  • Clustering
  • Data Mining
  • Edit Distances
• Machine Learning
  • Regression
  • Classification
  • Clustering
  • Using metrics
• Linear Algebra
• Data Preprocessing
• Data Mining
• Metrics
• State Space Search
• Natural Language Processing

Tutorials

Linear Regression

• Using Linear Regression for predicting the price of a house using the Boston Dataset

Logistic Regression

• Using Logistic Regression for saying if someone has diabetes based on its physical conditions

Clustering

• Using K-Means Clustering Machine Learning Algorithm - Simple Example
• Clustering Users of a Credit Card Company using the K-Means Algorithm
• Image segmentation using K-Kmeans
• Hierarchical clustering

Data Mining

• Market Basket Analysis Using A-Priori

Edit Distances

• Edit distances: Understanding them and Examples

Machine Learning

Regression

• Linear regression
• Logistic regression
• Support Vector Machines

Classification

• Decision Tree Model
• Naive Bayes Classifier
• K-Nearest Neighbors

Clustering

• K Means
• Hierarchical Clustering (WIP)
• Gaussian Mixture Model (WIP)

Using metrics

• Measuring the accuracy of a model

Linear Algebra

• Linear Algebra
• Pharo LAPACK

Data Preprocessing

• Normalization
• Random Partitioner for Datasets

Data Mining

• A Priori algorithm

Metrics

• Metrics
• Edit distances

State Space Search

• Graphs algorithms

Natural Language Processing

• Natural language Processing (WIP)
• Term Frequency - Inverse Document Frequency (TF-IDF)
• N-gram Model (WIP)
• Spelling correction (WIP)