jcatanza Goto Github PK

causalml

A course on causal machine learning.

course-nlp

A Code-First Introduction to NLP course

course-v4-working

a sandbox to play with the notebooks

covid-19

Download, plot and explore daily COVID-19 time series data for confirmed cases and deaths, from Johns Hopkins University repository.

data-exercise

A brief exercise in exploratory data analysis and modeling

ds-assignment-submission-practice-repository

e2e-ml-app-pytorch

🚀 An end-to-end ML applications using PyTorhc, W&B, FastAPI, Docker, Streamlit and Heroku → https://e2e-ml-app-pytorch.herokuapp.com/ (may take few minutes to spin up occasionally).

fastai-a-code-first-introduction-to-natural-language-processing-twiml-study-group

For the TWiML NLP Study Group. We review the fast.ai course "A Code-First Introduction to Natural Language Processing", created by Rachel Thomas, of The Data Institute | University of San Francisco. This repository contains the original Jupyter notebooks, plus annotated versions (with suffix `_jcat.ipynb`), as well as other materials I am developing for the Study Group, such as slide decks for the weekly Zoom meetups.

fastai-deep-learning-from-the-foundations-twiml-study-group

Review materials for the TWiML Study Group. Contains annotated versions of the original Jupyter noteboooks (look for names like *_jcat.ipynb ), slide decks from weekly Zoom meetups, etc.

fastai-practical-deep-learning-for-coders-twiml-study-group

Annotated, refactored notebooks and other materials created for the Fastai course; also has the original notebooks pulled from Fastai's git repository on 1/07/2020

fastai_notebooks

Experiments with fastai

good_wines_bad_wines

classifying wines with machine learning

gradient_boosting_regression

In this notebook, we'll build from scratch a gradient boosted trees regression model that includes a learning rate hyperparameter, and then use it to fit a noisy nonlinear function.

infogan

kaggle-avazu-clickthrough-rate-prediction

Python kernels for exploratory data analysis, feature engineering, modeling and evaluation, using two different approaches: gradient boosting machines with LightGBM, and logistic regression.

logistic-regression

The Logic of Logistic Regression: A Tutorial

makeintensivejan2021

Materials for MakeIntensive January 2021

makeschoolprojecttemplate

malaria-imaging-investigation

Investigative data science/machine learning guided tutorial on Kaggle malaria imaging dataset.

modern-slavery-statements-research

A collection of resources and inquiries advancing the research on modern slavery statements published by global commercial organisations

scientific-visualization-book

An open access book on scientific visualization using python and matplotlib

seattle-911

In this mini data science tutorial our task is to predict reasons for 911 calls, given a fictitious 911 calls database. We'll build and test a Random Forest model using Python and scikit-learn.

seattle-911-md-gist

Gist to convert the Jupyter notebook from the seattle-911 repository to a Medium post. Available at https://medium.com/@jcatanz/call-911-ab79e31690f6.

stan_kepler_populations

pyStan Hierarchical Bayesian Model that incorporates planet radius uncertainty into exoplanet occurrence rate calculations. Code prior to Sept 2016 was primarily developed by Joseph Catanzarite.

utilities

utility notebooks