Introduction to Machine Learning & Deep Learning
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Updated
Jan 3, 2020 - HTML
Introduction to Machine Learning & Deep Learning
This repository houses the files related to my homework assignments for the Multivariate Analysis class. Throughout the coursework, I utilized R Studio for all of my work. In addition to the homework, I also completed two projects as part of this course. Feel free to explore the files and projects included here to gain insights into the MVA class.
This repository contains code for my Machine Learning Basic Nanodegree Project.
Machine Learning Class at Harding Univeristy
Social Media Analysis using multivariate analysis in a Rutgers Business School Classroom
Illustrative analysis showing how population genetic structure discovery in Plasmodium genetic data using principal coordinates analysis and hierarchical clustering is sensitive to the pairwise genetic distance and the clustering linkage function
📙 End-to-end NLP and data visualization pipeline of the text from a machine learning textbook.
A simple web app that uses a trained sequential neural net to predict the rating of a hotel review.
A ggplot2 based biplot for principal components-like methods
A topic designed by Warwick Business School requires students to enhance loan portfolio management by utilising cluster analysis to group borrowers with similar characteristics, enabling personalised loan products, targeted marketing strategies, and a better customer support process to serve the unique needs of each segment through cluster analysis
Apply unsupervised learning techniques to identify customers segments.
Slides, exercises, and exams for my course "Statistical Learning with R" (Ecole Normale Supérieure Paris-Saclay, 2023)
Project on real-time proprietary data for Bertelsmann Arvato Analytics to identify customer segments that form the core customer base of the company using unsupervised learning techniques. Data cleaning was an integral part of the project since the data used here was real-world. Techniques like Principal Component Analysis were also used for Dim…
Unsupervised clustering of the UCI Wholesale Customer Spending Dataset
code for Visualizing and Understanding the Relationship between PCA, Auto encoder and K-Means Clustering.
Scouting the EPL: Player Clusters in the English Premier League
used the 'Breast Cancer Wisconsin (Diagnostic) Database', as source for the ML model, to predict if a cancer is either Benign or Malignant, based on many features.
Multi-class Classification of physiological features into 4 classes.
Machine Learning Project to build an algorithm which identifies Enron Employees who may have committed fraud based on the public Enron financial and email dataset.
Project 3 of Udacity MLND.
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