- General
- Artificial Intelligence
- Automation
- Ethics / altruistic motives
- Golang
- Java
- Julia, Python & R
- JavaScript
- Visualisation
- Mathematica & Wolfram Language
- Mathematics, Statistics, Probability & Probabilistic programming
- Data
- Graphs
- Examples
- Notebooks
- Models
- Articles, papers, code, data, courses
- Other Tools
- Presentations
- Best Practices
- Cheatsheets
- Misc
- Contributing
- Demystification of the key concepts of Artificial Intelligence and Machine Learning
- 12 thought leaders on LinkedIn who are creating original content to learn Artificial Intelligence and Machine Learning
- AI Repository by Goku Mohandas
- Digital Twins: Bringing artificial intelligence to Engineering
- See Artificial Intelligence
- Automated Machine Learning — An Overview
- Automated pipelines
- Automated machine learning tools (or partial AutoML tools)
- Automated Machine Learning - Google search results
- Recipes for Driverless AI
- PyCaret
- [PyCaret Tutorial Using Titanic Dataset](https://www.kaggle.com/ravileo/pycaret-tutorial-using-titanic-dataset](https://towardsdatascience.com/announcing-pycaret-an-open-source-low-code-machine-learning-library-in-python-4a1f1aad8d46)
- [PyCaret Demo](https://pycaret.org/demo/](https://github.com/pycaret/pycaret-demo-dataraction)
- Write and train your own custom machine learning models using PyCaret
- Running Low on Time? Use PyCaret to Build your Machine Learning Model in Seconds
- Build with PyCaret, Deploy with FastAPI: LinkedIn | TDS post
- Supercharge your Machine Learning Experiments with PyCaret and Gradio
- PyCaret and Streamlit: How to Create and Deploy Data Science Web App
- H2O Wave is a software stack for building beautiful, low-latency, realtime, browser-based applications and dashboards entirely in Python without using HTML, Javascript, or CSS
- Introduction to Regression in Python with PyCaret
- Regression with PyCaret: A better machine learning library
- Introduction to Binary Classification with PyCaret
- Classification with PyCaret: A better machine learning library
- Predict Customer Churn (the right way) using PyCaret
- Build and deploy machine learning web app using PyCaret and Streamlit
- Deploy Machine Learning App built using Streamlit and PyCaret on Google Kubernetes Engine
- Easy MLOps with PyCaret + MLflow
- Deploy PyCaret and Streamlit app using AWS Fargate — serverless infrastructure
- Predict Lead Score (the Right Way) Using PyCaret
- Deploy PyCaret Models on Edge Devices with ONNX Runtime
- Deploy Machine Learning Pipeline on cloud using Docker Container
- Predicting Spotify Song Popularity
- Predict Crash Severity with Machine Learning?
- Pycaret articles on Medium
- Libra • Automates the end-to-end machine learning process in just one line of code: GitHub | Notebooks with tutorials | Docs | NLP Queries
- GitHub is the best AutoML you will ever need 👇 👇 👇
- AutoGOAL: an autoML framework (high & low level) by Alejandro Piad et al.
- OttoML - Otto makes machine learning an intuitive, natural language experience.
- TPOT for Automated Machine Learning in Python
- Abacus AI workshops
- How to Use AutoKeras for Classification and Regression | AutoKeras Website
- Snorkel: Interact with the modern ML stack by programmatically building and managing training datasets: Snorkel Superglue | Author page
- Build machine learning powered applications without a data scientist
- A delightful machine learning tool that allows you to train/fit, test and use models without writing code
- Automated Machine Learning (AutoML) Libraries for Python
- Auto sklearn
- OpenML
- Lightning Flash - a collection of production ready Tasks for fast prototyping, baselining, finetuning and solving problems with deep learning built on top of PyTorch Lightning
- Aim - a super-easy way to record, search and compare AI experiments. With Aim you can compare 100s of experiments in no time!
- Data science competitions to build a better world
- An ethics checklist for data scientists
- 👉A Practical guide to Responsible Artificial Intelligence (AI) by PwC 👈
- Data ethics literacy cards by Anisha Fernando | Join the Slack community | The Private Lives of Data: YouTube video
- [Ethics in Artificial Intelligence](https://www.linkedin.com/posts/vincentg_ethics-in-artificial-intelligence-activity-6690365775091445761-U5qh
- robotethics | aiethics.ai | AIethics.AI – Artificial Intelligence and Robot Ethics
- UK gov’s guidance
- Google principles
- Go binding for Tensorflow
- Why to deploy ML model with Go
- GoLearn - Machine Learning library for Go
- Hands on Deep learning with Go - Github repo
- GDeep - Deep learning library written in golang
- Go-Torch Go binding for pytorch
- Gorgonia - ML in Go
- GoBrain - Simple NN written in Go
- Go Scientific Library
See Java
See JavaScript
See Visualisation
See Mathematica & Wolfram Language
See Mathematics, Statistics, Probability & Probabilistic programming
- Do we know our data...
- Data Science at the Command Line | References | on GitHub | Docker image with 80 CLI tools | Appendix: List of Command-Line Tools | Linux Command-Line resource by Chris Albon
- Awesome Datascience
- Awesome Learn Datascience
- Data Science for Dummies
- Data Science resources (scattered across the page)
- Learn Data Science by bitgrit
- and other related topics: Stats, Visualisations, Cheatsheets, etc...
- How can I become a data scientist?
- Being a Data Science Contractor - UK: How to find work?
- How to switch career from Automation Testing to Data Science? Here is a simple guide.
- 9 Mistakes to avoid when starting your career in Data Science
- How can I become a data scientist?
- 8 essential tools for data scientists
- Data Scientist is not One-Man-Army, but should know some tech concept, not mandatory to master (depend on the company), this is what I choose
- The Ultimate Learning Path to Become a Data Scientist and Master Machine Learning
♦️ MUST READ ARTICLES FOR DATA SCIENCE ENTHUSIAST♦️
- PyTorch Geometric Temporal - temporal extensions PyTorch Geometric Benedek Rozemberczki
- A number of interesting links on Graph Networks by Yaz
- Graph Representation Learning Book • The field of graph representation learning has grown at an incredible (and sometimes unwieldy)
- Daniele Grattarola gave a great talk on his graph machine learning library Spektral. Learn how to create graph neural networks (GNNs) with ease
- Towards Deeper Graph Neural Networks • Deep Adaptive Graph Neural Network (DAGNN) can be used to learn graph node representations from larger receptive fields.
- Graph-Powered Machine Learning • Free eBook Excerpt (Chapter: 3, 4, 7)
- Cytoscape interactive network visualization in Python and Dash. A graph visualization component for creating easily customizable, high-performance, interactive, and web-based networks.
- This dash app allows you to annotate automatically segmented brain regions
- Notes on graph theory — Centrality measures by Anas AIT AOMAR
- COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce - A new dataset for conversational recommendation over knowledge graphs in e-commerce platforms.
- @plotlygraphs We’ve explored @OpenAI’s new #GPT3 API, and we are super impressed with its capabilities!
- Natural Graph Networks • Conventional neural message passing algorithms are invariant under permutation of the messages and hence forget how the information flows through the network.
- Extracting knowledge from knowledge graphs using Facebook Pytorch BigGraph
- Graph-based, Self-Supervised Program Repair from Diagnostic Feedback
- Graph Programming by Uri Valevski https://bit.ly/3nhZr4w
- Open Graph Benchmark: Datasets for Machine Learning on Graphs -
- BCS APSG - 2019 02 14 How Graph Technology is Changing AI and ML at BCS London
- Language Generation with Multi-hop Reasoning on Commonsense Knowledge Graph
- Graph databases
- See the Grakn example in the
examples/data/databases/graph/graknfolder
- See the Grakn example in the
See Notebooks
- Model Zoo - Discover open source deep learning code and pretrained models
- Model Zoo: Caffe docs | Caffe | MXNet | DL4J | CoreNLP
See Articles, papers, code, data, courses
See Other Tools
- "nn" things every Java Developer should know about AI/ML/DL
- From backend development to machine learning
- Tribuo: an introduction to a Java ML Library
- "nn" things every Java Developer should know about AI/ML/DL
- Looking into Java ML/DL libraries: Tribuo and DeepNetts
- NLP presentations
- Data presentations
- Best Practices for ML Engineering by Martin Zinkevich
- See also Best practices / rules / an unordered list of high level or low level guidelines
See Cheatsheets
See Misc
Contributions are very welcome, please share back with the wider community (and get credited for it)!
Please have a look at the CONTRIBUTING guidelines, also have a read about our licensing policy.
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