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Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"

Machine Learning ⭐ 117

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, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.

Text Analysis ⭐ 32

Explaining textual analysis tools in Python. Including Preprocessing, Skip Gram (word2vec), and Topic Modelling.

Ml Simulations ⭐ 28

Animated Visualizations of Popular Machine Learning Algorithms

Data Science Skills Practice ⭐ 11

Armadillo Gmm ⭐ 10

gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation

Projects ⭐ 10

This is the repository containing machine learning and deep learning projects, as well as some presentation slides on these topics.

Machine Learning Clustering Retrieval ⭐ 6

Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python

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