{"id":15359397,"url":"https://github.com/jaidevd/talentsprint-workshop","last_synced_at":"2025-08-02T23:30:43.117Z","repository":{"id":143545879,"uuid":"110698456","full_name":"jaidevd/talentsprint-workshop","owner":"jaidevd","description":"TalentSprint workshop on Machine Learning in November 2017","archived":false,"fork":false,"pushed_at":"2017-11-17T10:52:54.000Z","size":595,"stargazers_count":1,"open_issues_count":1,"forks_count":30,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-10-16T04:22:34.437Z","etag":null,"topics":["data-science","jupyter-notebook","machine-learning","python","sklearn","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jaidevd.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-11-14T14:09:42.000Z","updated_at":"2022-04-03T22:26:56.000Z","dependencies_parsed_at":"2023-06-08T23:10:54.600Z","dependency_job_id":null,"html_url":"https://github.com/jaidevd/talentsprint-workshop","commit_stats":{"total_commits":19,"total_committers":1,"mean_commits":19.0,"dds":0.0,"last_synced_commit":"c2763f6cd7a0070ec0d1e3ccb12b180cf546c94c"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaidevd%2Ftalentsprint-workshop","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaidevd%2Ftalentsprint-workshop/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaidevd%2Ftalentsprint-workshop/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaidevd%2Ftalentsprint-workshop/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jaidevd","download_url":"https://codeload.github.com/jaidevd/talentsprint-workshop/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":228501308,"owners_count":17930229,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","jupyter-notebook","machine-learning","python","sklearn","tutorial"],"created_at":"2024-10-01T12:44:38.700Z","updated_at":"2024-12-06T17:25:54.321Z","avatar_url":"https://github.com/jaidevd.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Here's a brief plan of the four sessions of the workshop. Each of these\nsections will include exercises based on real-world datasets. While most of the\nworkshop depends only on scikit-learn, there are a few other requirements too.\nAn exhaustive list of Python packages required for the workshop is as follows.\n\nRequirements:\n-------------\n- NumPy\n- SciPy\n- Matplotlib\n- Pandas\n- scikit-learn\n- tensorflow\n- keras\n- theano\n\nAt most a couple more cursory packages might get added to this list as I\nproceed with creating the material, but those should be easily installable at\nthe venue itself, assuming that the participants have a Python distribution\nlike Enthought Canopy or Anaconda installed.\n    \nSaturday Pre-Lunch\n------------------\n\n* Inbuilt dataset loading utilities\n* Introduction to the estimator object\n* Basic classification \u0026 regression tasks\n* Introduction to supervised and unsupervised learning\n\nSaturday Post-Lunch\n-------------------\n\n* Linear vs Nonlinear models\n* Kernel Methods in Machine Learning\n* Feature selection \u0026 Dimensionality Reduction\n* Interpreting a trained model\n\nSunday Pre-Lunch\n----------------\n\n* Measuring model performance\n* Cross validation\n* Grid and random parameter search\n\nSunday Post-Lunch\n-----------------\n\n* Gradient descent and its variations\n* Introduction to neural networks\n* Building a shallow neural network\n* Brief introduction to deep learning\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaidevd%2Ftalentsprint-workshop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjaidevd%2Ftalentsprint-workshop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaidevd%2Ftalentsprint-workshop/lists"}