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asl_recognition

Convolutional neural network to classify images of letters from American Sign Language.

busca-a-para-labirintos

Busca A* para resolução de labirintos em python, com função de plot e testes de performance.

chest-x-ray-classification

Artificial intelligence (AI)-powered chest disease detection and classification.

classify-song-genres

Using a dataset comprised of songs of two music genres (Hip-Hop and Rock), you will train a classifier to distinguish between the two genres based only on track information derived from Echonest (now part of Spotify).

climate_change_on_birds

Our analysis will use data from the UK Met Office together with records from the Global Biodiversity Information Facility to build our very own species distribution model using machine learning. This model will be able to predict where our bird species of interest is likely to occur in the future - information that is invaluable to conservation organization working on the ground to preserve these species and save them from extinction! In this notebook, we will model the Scottish crossbill (Loxia scotica). The Scottish crossbills is a small bird that inhabits the cool Scottish forests and feeds on pine seeds. Only ~ 20,000 individuals of this species are alive today. The code and the data sources in this project can be reapplied to any other species we may be interested in studying.

clustering_heart_disease

Doctors frequently study former cases in order to learn ways to best treat their patients. A patient who has a similar health history or symptoms to a previous patient could benefit from undergoing the same treatment. This project investigates whether doctors might be able to group together patients to target treatments using some common unsupervised learning techniques.

dash_upload

data-version-control

extract_stock_sentiment

In this notebook, we will generate investing insight by applying sentiment analysis on financial news headlines from FINVIZ.com. Using this natural language processing technique, we can understand the emotion behind the headlines and predict whether the market feels good or bad about a stock. It would then be possible to make educated guesses on how certain stocks will perform and trade accordingly. (And hopefully, make money!)

fastbook

Draft of the fastai book

google_colab

Google colab tests

hamilton

Test a general purpose micro-framework for specifying dataflows

heart_rate

Examine the relationship between the maximum heart rate one can achieve during exercise and the likelihood of developing heart disease.

hottest_topics_machine_learning

Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.

john-snow-s-ghost-map

Recreating John Snow's Ghost Map

keras

Deep Learning for humans

lecturas

Este repositorio es una recopilación de lecturas.

life_expectancy

Compare life expectancy across countries and genders with ggplot2.

lightpipes

LightPipes for Python, "Pure Python version"

manimations

markdown-badges

Badges for your personal developer branding, profile, and projects.

minmax-e-alfa-beta

ml-webapp-using-streamlit

A WebApp to depict an Binary Classification Machine Learning Model on Mushrooms showing whether it is edible or not using streamlit

network-analysis-game-of-thrones

Jon Snow, Daenerys Targaryen, or Tyrion Lannister? Who is the most important character in Game of Thrones? Let's see what mathematics can tell us about this! In this project, you will look at the character co-occurrence network and its evolution over the five books in R.R. Martin's hugely popular book series A Song of Ice and Fire (perhaps better known as the TV show Game of Thrones). You will look at how the importance of the characters changes over the books using different centrality measures.

nips-papers

Titles, authors, abstracts, and extracted text for all NIPS papers (1987-2017)

python-science-tutorial

Series of notebooks to illustrate different plotting features using Python

readme-template

README.md template for your open-source project

road_sign_knn

Recognizing a road sign with kNN

sharpe_ratio

An investment may make sense if we expect it to return more money than it costs. But returns are only part of the story because they are risky - there may be a range of possible outcomes. How does one compare different investments that may deliver similar results on average, but exhibit different levels of risks?

simpson