{"id":18339980,"url":"https://github.com/mxagar/data_science_python_tools","last_synced_at":"2025-04-09T20:39:28.543Z","repository":{"id":106857281,"uuid":"430127713","full_name":"mxagar/data_science_python_tools","owner":"mxagar","description":"This repository collects most important ML \u0026 DS tools in annotated Jupyter Notebooks.","archived":false,"fork":false,"pushed_at":"2023-02-28T14:34:17.000Z","size":39529,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T12:50:46.843Z","etag":null,"topics":["data-science","deep-learning","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mxagar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2021-11-20T14:40:57.000Z","updated_at":"2023-02-28T14:26:14.000Z","dependencies_parsed_at":null,"dependency_job_id":"6ec465ce-f24d-4968-bc49-a9c310ff8e41","html_url":"https://github.com/mxagar/data_science_python_tools","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdata_science_python_tools","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdata_science_python_tools/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdata_science_python_tools/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdata_science_python_tools/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mxagar","download_url":"https://codeload.github.com/mxagar/data_science_python_tools/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248109371,"owners_count":21049300,"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","deep-learning","machine-learning"],"created_at":"2024-11-05T20:20:22.542Z","updated_at":"2025-04-09T20:39:28.522Z","avatar_url":"https://github.com/mxagar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Science Tools in Python\n\nThis project contains notebooks and notes related to the most important concepts and tools necessary for **machine learning** and **data science**.\n\nI started collecting most of the notebooks and notes while following several web tutorials and Udemy courses, such as:\n\n- [Python for Data Sciene and Machine Learning Bootcamp (by José Marcial Portilla)](https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/)\n- [Complete Tensorflow 2 and Keras Deep Learning Bootcamp (by José Marcial Portilla)](https://www.udemy.com/course/complete-tensorflow-2-and-keras-deep-learning-bootcamp/)\n- [Python for Computer Vision with OpenCV and Deep Learning (by José Marcial Portilla)](https://www.udemy.com/course/python-for-computer-vision-with-opencv-and-deep-learning/)\n- [Practical AI with Python and Reinforcement Learning (by José Marcial Portilla)](https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/)\n- [Machine Learning A-Z™: Hands-On Python \u0026 R In Data Science (by Kirill Eremenko \u0026 Hadelin de Ponteves)](https://www.udemy.com/course/machinelearning/)\n\nUnfortunately, sometimes I have not found a repository to fork, so the attribution is done in this `README.md`.\n\nThe aforementioned courses are very practical, they don't focus so much on the theory; for that purpose, I used:\n\n- \"An Introduction to Statistical Learning with Applications in R\", by James et al. A repository with python notebooks can be found in [https://github.com/JWarmenhoven/ISLR-python](https://github.com/JWarmenhoven/ISLR-python).\n- \"Reinforcement Learning\" by Sutton \u0026 Barto.\n- \"Pattern Recognition and Machine Learning\" by Bishop. A repository with python notebooks can be found in [https://github.com/ctgk/PRML](https://github.com/ctgk/PRML).\n\nNote that in some cases I also just simply followed the documentation provided in the websites of the used packages.\n\nImportant related `howto` files (not public) of mine are (for my personal tracking):\n\n- `~/Dropbox/Learning/PythonLab/python_manual.txt`\n- `~/Dropbox/Documentation/howtos/sklearn_scipy_sympy_stat_guide.txt`\n- `~/Dropbox/Documentation/howtos/keras_tensorflow_guide.txt`\n- `~/Dropbox/Documentation/howtos/pybullet_openai_guide.txt`\n- `~/Dropbox/Documentation/howtos/python_reinforcement_learning_openai.txt`\n\nTo run the notebooks locally, first, install an environment manager, e.g., [conda](https://docs.conda.io/en/latest/), create an environment and install the required dependencies:\n\n```bash\n# Create your env\nconda create --name ds pip python=3.8\nconda activate ds\n\n# Install all necessary packages\n# FIXME: Many packages can be removed\npip install -r requirements.txt\n```\n\nThen, you open the notebooks; if I were a beginner, I'd start sequentially.\n\nSee also:\n\n- An 80/20 guide for Data Processing: Data Cleaning, Exploratory Data Analysis, Feature Engineering, Feature Selection \u0026mdash; [eda_fe_summary](https://github.com/mxagar/eda_fe_summary).\n- My notes and the code of the IBM Machine Learning Professional Certificated offered by IBM \u0026 Coursera \u0026mdash; [machine_learning_ibm](https://github.com/mxagar/machine_learning_ibm).\n\nMikel Sagardia, 2018.  \nNo guarantees.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fdata_science_python_tools","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmxagar%2Fdata_science_python_tools","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fdata_science_python_tools/lists"}