- General
- Artificial Intelligence
- Automation
- Ethics / altruistic motives
- 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
- See Artificial Intelligence
- Automated Machine Learning — An Overview
- Automated pipelines
- Automated machine learning tools (or partial AutoML tools)
- Automated Machine Learning - Google search results
- 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
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♦️
- A number of interesting links on Graph Networks by Yaz
- Graph databases
- See the Grakn example in the
examples/data/databases/graph/graknfolder
- See the Grakn example in the
- BCS APSG - 2019 02 14 How Graph Technology is Changing AI and ML at BCS London
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
- 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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