Training and evaluating a variational autoencoder for pan-cancer gene expression data

ECN 5090- Machine Learning in Economics and Finance (Python)

Lectures for Introduction to Data Science for Public Policy (PPOL 670-01)

Unsupervised ML: Finding Customer Segments in General Population

Libro gratuito de inteligencia artificial en español, incluye temas como algoritmos genéticos, programación lógica y machine learning.

It is One of the Easiest Problems in Data Science to Detect the MNIST Numbers, Using a Classification Algorithm, Here I have used a csv File which contains the Pixels of the Numbers from 0 to 9 and we have to Classify the Numbers Accordingly. I have Used K-Means Classification Algorithm.

Creating Customer Segments - 4th project for Udacity's Machine Learning Nanodegree

Civic Issue Detection Dataset from Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues

Mini blog for notes and guides on Machine Learning (Open Notes)

Machine Learning for Data Science lecture at Freie University Berlin during WiSe21/22

Discovery and Learning of Minecraft Navigation Goals from Pixels and Coordinates

Easy-to-use collection of statistical methods and techniques, all written in R 🗂

Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.

NUS Deep Unsupervised Learning course webpage (CS6101-1920). For Semester I, 2019/2020.

A framework to analyze, visualize abd predict scientific trends

Projects which were completed as part of assignments of Great Learning's PGP in Artificial Intelligence and Machine Learning

This repo contains different projects in the field of Data Science (Workload: 280 h). It delves into supervised, unsupervised, and deep learning. It also deals with the most common tasks of data scientists.

Use Unsupervised Learning method to extract different customer segments based on their buying pattern and their behaviour

In this project I use unsupervised learning techniques to identify different segments of costumers with different preferences for optimizing product delivery.