Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

Machine learning algorithms in Dart programming language

A simple machine learning framework written in Swift 🤖

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

次元期权应征面试题范例。 #易经 #道家 #十二生肖 #姓氏堂号子嗣贞节牌坊 #天文历法 #张灯结彩 #农历 #夜观星象 #廿四节气 #算卜 #紫微斗数 #十二时辰 #生辰八字 #命运 #风水 《始祖赢政之子赢家黄氏江夏堂联富•秦谏——大秦赋》 万般皆下品，唯有读书高。🚩🇨🇳🏹🦔中科红旗，歼灭所有世袭制可兰经法家回教徒巫贼巫婆、洋番、峇峇娘惹。https://gitee.com/englianhu

A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

🤠 📿 The Highly Adaptive Lasso

A repository for a machine learning project about developing a hybrid movie recommender system.

Analyzes weightlifting videos for correct posture using pose estimation with OpenCV

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

An R package for modern methods for non-probability surveys

Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.

TwitPersonality: Computing Personality Traits from Tweets using Word Embeddings and Supervised Learning

Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) …

Iterative shrinkage / thresholding algorithms (ISTAs) for linear inverse problems