emykes

emykes's Stars

• temporaer/tutorial_ml_gkbionics

  A Tutorial on Simple Machine Learning Methods Held for the Graduate School on Bionics, 2012

  Language:TeX7493

• jupyter/jupyter

  Jupyter metapackage for installation, docs and chat

  Language:Python14.9k4.1k

• onimur/onimur

  52105

• adam-p/markdown-here

  Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.

  Language:JavaScript59.6k11.3k

• emykes/Flu_Vaccination_ML

  The aim of this study is to predict how likely individuals are to receive their H1N1 flu vaccine. We believe the prediction outputs (model and analysis) of this study will give public health professionals and policy makers, as an end user, a clear understanding of factors associated with low vaccination rates. This in turn, enables end users to systematically act on those features hindering people to get vaccinated.

  Language:Jupyter Notebook51

• Shoemaker703/time_series_project

  Language:Jupyter Notebook12

• emykes/SkinCancerImageClassification

  The aim of this project is to use Convolutional Neural Networks (CNNs) to distinguish dermoscopic images of malignant skin lesions from benign lesions.

  Language:Jupyter Notebook21

• soulmachine/machine-learning-cheat-sheet

  Classical equations and diagrams in machine learning

  Language:TeX7.4k1.3k

• fivethirtyeight/data

  Data and code behind the articles and graphics at FiveThirtyEight

  Language:Jupyter Notebook16.8k10.9k

• ResidentMario/geoplot

  High-level geospatial data visualization library for Python.

  Language:Python1.2k95

• emykes/ds-classification_workflow_demo-pdu32

  1

• emykes/dsc-phase-3-choosing-a-dataset

  1

• emykes/DSLE-0830-Pipelines-ClassificationEvaluation

  Language:Jupyter Notebook1

• emykes/handson-ml2

  A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

  1

• anishmahapatra/masters-machine-learning-1

  Codes encompassing topics on the realm of linear regression (simple and multiple), logistic regression and clustering using pca

  Language:Jupyter Notebook42

• mdeff/fma

  FMA: A Dataset For Music Analysis

  Language:Jupyter Notebook2.2k439

• aleximmer/Laplace

  Laplace approximations for Deep Learning.

  Language:Python46872

• aamini/introtodeeplearning

  Lab Materials for MIT 6.S191: Introduction to Deep Learning

  Language:Jupyter Notebook7.3k3.6k