Training eSupport System
  • Log In
    • Log in with LS Login
    • Login
    • Register
  • About
  • Events
  • Materials
  • e-Learning
  • Workflows
  • Collections
  • Learning paths
  • Directory
    • Providers
    • Nodes
    • Spaces

TeSS makes use of some necessary cookies to provide its core functionality. Additionally, we make use of Google Analytics to discover how people are using TeSS in order to help us improve the service. To opt out of this, choose the "Allow necessary cookies" option.

See our Privacy Policy for more information.

You can modify your cookie preferences at any time here, or from the link in the footer.

Allow necessary cookies Allow all cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month
    • Scientific topic
    • Active learning5
    • Ensembl learning5
    • Kernel methods5
    • Knowledge representation5
    • Machine learning5
    • Neural networks5
    • Recommender system5
    • Reinforcement learning5
    • Supervised learning5
    • Unsupervised learning5
    • Bayesian methods2
    • Biostatistics2
    • Descriptive statistics2
    • Gaussian processes2
    • Inferential statistics2
    • Markov processes2
    • Multivariate statistics2
    • Probabilistic graphical model2
    • Probability2
    • Statistics2
    • Statistics and probability2
    • Data rendering1
    • Data visualisation1
    • Single-cell genomics1
    • Single-cell sequencing1
    • Show N_FILTERS more
    • Content provider
    • Glittr.org5
    • Show N_FILTERS more
    • Keyword
    • Machine learning
    • Python3
    • R3
    • Single-cell sequencing2
    • Statistics2
    • Artificial intelligence1
    • Cloud computing1
    • Data science1
    • Data visualization1
    • Enrichment analysis1
    • General1
    • Large language models1
    • Nextflow1
    • Pathways and Networks1
    • RNA-seq1
    • Rust1
    • Shiny1
    • Transcriptomics1
    • Workflows1
    • Show N_FILTERS more
    • Competency level
    • Not specified
    • Show N_FILTERS more
    • Licence
    • Apache License 2.0
    • License Not Specified32
    • Creative Commons Attribution 4.0 International11
    • MIT License10
    • Creative Commons Attribution Share Alike 4.0 International6
    • GNU General Public License v3.0 only1
    • Show N_FILTERS more
    • Author
    • Maxime Labonne1
    • PAIR code1
    • Salvatore Raieli1
    • fast.ai1
    • posit-conf-20231
    • Show N_FILTERS more
    • Contributor
    • Abraham Omorogbe1
    • Adam Pearce1
    • Andy Coenen1
    • Bozhao1
    • Daniel1
    • Daniel Andrade1
    • Hamel Husain1
    • Isaac Flath1
    • Jan Van de Poel1
    • Jeremy Howard1
    • Joe Bender1
    • Joe Dockrill1
    • Joshua Robison1
    • Karel Ha1
    • Kerrick Staley1
    • Kyle Willett1
    • Marie-Helene Burle1
    • Maxime Labonne1
    • Mine Cetinkaya-Rundel1
    • Nikhil Maddirala1
    • Pietro Monticone1
    • Rajiv Pasricha1
    • Ruben1
    • Salvatore Raieli1
    • Sebastian Raschka1
    • Shrinija Kummari1
    • TheJChaps1
    • Theiss Heilker1
    • Vineet Ahuja1
    • Vishnu Subramanian1
    • Zero to Singularity1
    • bxbrenden1
    • ezeeetm1
    • micstn1
    • mone271
    • zenlytix1
    • zzweig1
    • Show N_FILTERS more
    • Node
    • Switzerland5
    • Show N_FILTERS more
  • Show materials from all spaces
  • Show disabled materials
  • Show materials with broken links
  • Show archived materials

Training materials

  • Subscribe via email

Email Subscription

Register training material

Keywords: Machine learning

and Competency level: Not specified

and Licence: Apache License 2.0

5 materials found
  • PAIR-code/understanding-umap

    ELIXIR node event
    Machine learning Statistics and probability Single-cell sequencing Data visualisation Single-cell sequencing Data visualization Data science Machine learning Statistics
  • mlabonne/llm-course

    ELIXIR node event
    Machine learning Large language models Python Machine learning Artificial intelligence
  • posit-conf-2023/python-modeling

    ELIXIR node event
    Machine learning Machine learning Python
  • SalvatoreRa/tutorial

    ELIXIR node event
    Machine learning General Machine learning
  • fastai/course20

    ELIXIR node event
    Machine learning Statistics and probability Machine learning Statistics
Training eSupport System
[email protected]
Contribute
About TeSS
Browse Spaces
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.1
Source code
API documentation
Bioschemas testing tool

TeSS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676559.