UBC Library Research Commons Open Workshops: The UBC Library provides workshop materials in open formats to view and use anytime. The materials cover: core skills in digital scholarship (Jekyll, Docker, Git, GitHub, Unix shell), data analysis and visualization (Tableau, R, Excel), and the Geographic Information Systems (GIS).

NIH Tools & Analytics: NIH's Office of Data Science Strategy (ODSS) works to adopt and adapt emerging and specialized technologies for data analytics. These technologies include APIs, cloud-ready software, and other tools to advance health. Additionally, the ODSS partners with other government agencies, industry, academia, and nonprofit organizations to support and broaden the use of specialized tools. Learn more about NIH efforts to advance tools and analytics.

Harvard Business School (HBS) 3 Applications of Data Analytics in Healthcare: HBS outlines what data analytics is, examples of how it applies to health care, common pitfalls to avoid, and how to build your data skills as a health care professional.

HashDork 8 Best Predictive Analytics Tools (Open-Source): This article goes over the idea of predictive analytics tools, their application, and a number of examples of open-source tools.

Google Cloud Natural Language AI: Analyze text with AI using pre-trained API or custom AutoML machine learning models to extract relevant entities, understand sentiment, and more. 

Google Cloud Using the Healthcare Natural Language API: This page explains how to enable the Healthcare Natural Language API, configure permissions, and call the analyzeEntities method to extract medical insights from medical text.

TensorFlow Basic Text Classification Tutorial: This tutorial demonstrates text classification starting from plain text files stored on disk. You'll train a binary classifier to perform sentiment analysis on an IMDB dataset. 

TensorFlow Word Embedding Tutorial: This tutorial contains an introduction to word embeddings. You will train your own word embeddings using a simple Keras model for a sentiment classification task, and then visualize them in the Embedding Projector.

TensorFlow Text Classification with BERT Tutorial: This tutorial contains complete code to fine-tune BERT to perform sentiment analysis on a dataset of plain-text IMDB movie reviews. In addition to training a model, you will learn how to preprocess text into an appropriate format.

Cortical.io NLP Keyword Extraction: This free tool allows users to extract keywords from text or from a web page. 

University of Oxford VGG Image Annotator: Oxford's Visual Geometry Group provides a simple and standalone manual annotation software for image, audio and video. This is an open srouce project based on HTML, Javascript and CSS. The tool is released under the BSD-2 clause license which allows it to be useful for both academic projects and commercial applications.

TensorFlow Image Classification Tutorial: This tutorial shows how to classify images of flowers. Users will gain practical experience with the following concepts: efficiently loading a dataset off disk; and identifying overfitting and applying techniques to mitigiate it, including data augmentation and dropout. 

TensorFlow Image Segmentation Tutorial: This tutorial focuses on the task of image segmentation, using a modified U-Net. 

TensorFlow Image Captioning With Visual Attention Tutorial: This tutorial provides basic step-by-step instructions to generating captions to a given image. 

Stanford ConvNetJS MNIST Demo: This demo trains a Convolutional Neural Network on the MNIST digits dataset in your browser with Javascript. The dataset is fairly easy and one should expect to get around 99% accuracy within a few minutes.

Stanford ConvNetJS CIFAR-10 Demo: This demo trains a Convolutional Neural Network on the CIFAR-10 dataset in your browser with Javascript. The state of the art on this dataset is about 90% accuracy and human performance is at about 94%.

TensorFlow Neural Network Playground: Open demonstration for beginners to make neural networks accessible and easy to learn. Controls are provided for users to tailor their experience to a specific topic or lesson.

Teachable Machine: Train a computer to recognize your own images, sounds & poses. An introductory experience in creating machine learning models.