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
    • Bayesian methods8
    • Biostatistics8
    • Descriptive statistics8
    • Gaussian processes8
    • Inferential statistics8
    • Markov processes8
    • Multivariate statistics8
    • Probabilistic graphical model8
    • Probability8
    • Statistics8
    • Statistics and probability8
    • Show N_FILTERS more
    • Tool
    • Galaxy8
    • scikit-learn5
    • Show N_FILTERS more
    • Content provider
    • GTN8
    • Show N_FILTERS more
    • Keyword
    • Statistics and machine learning
    • español6
    • microgalaxy6
    • Assembly5
    • Introduction to Galaxy Analyses5
    • Single Cell5
    • deutsch5
    • italiano5
    • Microbiome4
    • Sequence analysis4
    • Using Galaxy and Managing your Data4
    • biodiversity4
    • metagenomics4
    • fair3
    • illumina3
    • interactive-tools3
    • plants3
    • 10x2
    • Contributing to the Galaxy Training Material2
    • FAIR Data, Workflows, and Research2
    • Foundations of Data Science2
    • Genome Annotation2
    • MIGHTS2
    • Variant Analysis2
    • assembly2
    • bacteria2
    • eukaryote2
    • jbrowse12
    • nanopore2
    • paper-replication2
    • Choose your own Adventure1
    • Climate1
    • CodeSpaces1
    • Command-line1
    • Digital Humanities1
    • GitPod1
    • InvenioRDM1
    • LORIS Score Model1
    • Machine Learning1
    • QC1
    • Tabular Learner1
    • Transcriptomics1
    • VGP1
    • animals1
    • beer1
    • binning1
    • citizen science1
    • covid191
    • deep-learning1
    • dephosphorylation-site-prediction1
    • diversity1
    • fine-tuning1
    • genome1
    • gmod1
    • how-to-cite-galaxy1
    • jupyter-lab1
    • jupyter-notebook1
    • machine-learning1
    • one-health1
    • pacbio1
    • taxonomic profiling1
    • text mining1
    • virology1
    • work-in-progress1
    • zenodo1
    • Show N_FILTERS more
    • Competency level
    • Beginner7
    • Intermediate1
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International8
    • Show N_FILTERS more
    • Target audience
    • Students8
    • Show N_FILTERS more
    • Author
    • Anup Kumar6
    • Alireza Khanteymoori2
    • Simon Bray2
    • Jeremy Goecks1
    • Junhao Qiu1
    • Kaivan Kamali1
    • Paulo Cilas Morais Lyra Junior1
    • Show N_FILTERS more
    • Contributor
    • Teresa Müller
    • Saskia Hiltemann30
    • Anup Kumar27
    • Björn Grüning27
    • Helena Rasche18
    • Martin Čech15
    • Bérénice Batut14
    • Armin Dadras11
    • Kaivan Kamali8
    • Paulo Cilas Morais Lyra Junior6
    • Alireza Khanteymoori5
    • Fabio Cumbo5
    • Wandrille Duchemin5
    • olisand5
    • Amirhossein Naghsh Nilchi4
    • Cristóbal Gallardo2
    • Delphine Lariviere2
    • Gildas Le Corguillé2
    • Junhao Qiu2
    • Michelle Terese Savage2
    • Nate Coraor2
    • Simon Bray2
    • qiagu2
    • Anthony Bretaudeau1
    • Bert Droesbeke1
    • Daniel Blankenberg1
    • Daniel Sobral1
    • Enis Afgan1
    • Mohammad Joudy1
    • Mélanie Petera1
    • Niall Beard1
    • Nicola Soranzo1
    • Pavankumar Videm1
    • Stella Fragkouli1
    • Vijay1
    • dlal-group1
    • Show N_FILTERS more
    • Resource type
    • e-learning6
    • slides2
    • Show N_FILTERS more
    • Related resource
    • Associated Training Datasets8
    • Associated Workflows6
    • 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: Statistics and machine learning

and Contributors: Teresa Müller

8 materials found
  • e-learning

    Galaxy Tabular Learner - Building a Model using Chowell clinical data

    •• Intermediate
    Statistics and probability LORIS Score Model Machine Learning Statistics and machine learning Tabular Learner
  • slides

    Fine-tuning Protein Language Model

    • Beginner
    Statistics and probability Statistics and machine learning
  • slides

    Introduction to Machine learning

    • Beginner
    Statistics and probability Statistics and machine learning
  • e-learning

    Fine tune large protein model (ProtTrans) using HuggingFace

    • Beginner
    Statistics and probability Statistics and machine learning deep-learning dephosphorylation-site-prediction fine-tuning interactive-tools jupyter-lab machine-learning
  • e-learning

    Regression in Machine Learning

    • Beginner
    Statistics and probability Statistics and machine learning
  • e-learning

    Classification in Machine Learning

    • Beginner
    Statistics and probability Statistics and machine learning
  • e-learning

    Basics of machine learning

    • Beginner
    Statistics and probability Statistics and machine learning
  • e-learning

    Deep Learning (Part 3) - Convolutional neural networks (CNN)

    • Beginner
    Statistics and probability Statistics and machine learning
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.