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2018-cs109b

Harvard CS109b Public Repository

ar-php

For of Ar-PHP (http://www.ar-php.org/project-php-arabic.html)

baby-food-selection-as-an-oop-application

In this project, I build a baby food menu whereby each food item on the menu triggers a response from the baby using Object-Oriented Programming.

building-a-recommendation-system-with-python-course

Course materials from the Building a Recommendation System course with Python offered through Linked-in.

calendar-year

This program prompts a user to specify a given four-digit year and then displays a calendar for each month in that year.

covid19-tracking

credit-card-balances

Programs to estimate credit card balances assuming a payment profile

credit-card-balances-revised

dalvarez83

Hi there, This is about me.

darts

Dynamic and Responsive Targeting System

deduplication-of-records

Deduplication of record linkage matching using biographical data and phonetics encoding in multiple languages

deep-learning-with-python-and-pytorch

Course material on developing Deep Learning models using PyTorch. The materials shows how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. The course then focuses on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). The course ends with a focus on dimensionality reduction and autoencoders. Including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications. This course was created by IBM.

docker-for-data-scientists

File repository for the Docker for Data Scientists course. `Docker` is a platform to develop, deploy, and run applications with containers. This is so that the application works on your computer or someone else's. This is important for reproducibility in data science/machine learning.

driverlessai-recipes

Recipes for Driverless AI

earth_ai_book_materials

The repo contains the source code, notebooks, and technical resources that assist students to read the book Artificial Intelligence in Earth Science.

fastbook

The fastai book, published as Jupyter Notebooks

gender_experiment

R Code and reporting output for randomized causal inference study to answer question on whether there is bias in following directions from gendered voices.

geokb

Data processing workflows for initializing and building the Geoscience Knowledgebase

geowrangler

🌏 A python package for wrangling geospatial datasets

how-to-use-mindat-api

Use Cases for Open Mindat Data API.

idb_costaricapoverty_eda

This Exploratory Data Analysis (EDA) took a Kaggle competition dataset sponsored by the Inter-American Development Bank (IADB) on predicting poverty levels amongst households in Costa Rica. The research idea is to identify and explore those variables (household attributes or assets found in homes) to understand the data structure and underlying relationships in the data.

intro-geospatial-data

Introductory tutorial covering core ideas and skills working with geospatial data.

islr

Introduction to Statistical Learning

lime

Lime: Explaining the predictions of any machine learning classifier

logistic-regression-and-classification

This repository presents examples of logistic regression and classification models

magasin

Cloud native open-source end-to-end data / AI / ML platform

microsoft-planetary-computer

The Planetary Computer combines a multi-petabyte catalog of global environmental data with intuitive APIs, a flexible scientific environment that allows users to answer global questions about that data, and applications that put those answers in the hands of conservation stakeholders.

mistakes-to-avoid-in-machine-learning

Sample code based on Mistakes to Avoid in Machine Learning course through Linkedin Learning.

nasa-tutorials

A collection of six Jupyter notebooks to illustrate how OPeNDAP can be used to access data served by NASA.

openai-quickstart-node

The OpenAI API can be applied to virtually any task that requires understanding or generating natural language and code. This is a quick start guide for building a pet name generator.