{"id":20200019,"url":"https://github.com/trainingbypackt/data-science-with-python","last_synced_at":"2025-07-31T08:05:34.449Z","repository":{"id":68103166,"uuid":"159273091","full_name":"TrainingByPackt/Data-Science-with-Python","owner":"TrainingByPackt","description":"Combine Python with machine learning principles to discover hidden patterns in raw data","archived":false,"fork":false,"pushed_at":"2019-07-04T08:15:26.000Z","size":36960,"stargazers_count":56,"open_issues_count":0,"forks_count":137,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-10T11:35:19.439Z","etag":null,"topics":["kera","matp","natural-language-processing","numpy","pandas","python","sci","tens"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/TrainingByPackt.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":"2018-11-27T03:52:55.000Z","updated_at":"2024-12-24T13:35:39.000Z","dependencies_parsed_at":"2023-04-17T21:16:35.319Z","dependency_job_id":null,"html_url":"https://github.com/TrainingByPackt/Data-Science-with-Python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/TrainingByPackt/Data-Science-with-Python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrainingByPackt%2FData-Science-with-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrainingByPackt%2FData-Science-with-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrainingByPackt%2FData-Science-with-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrainingByPackt%2FData-Science-with-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TrainingByPackt","download_url":"https://codeload.github.com/TrainingByPackt/Data-Science-with-Python/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrainingByPackt%2FData-Science-with-Python/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268009961,"owners_count":24180459,"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","status":"online","status_checked_at":"2025-07-31T02:00:08.723Z","response_time":66,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["kera","matp","natural-language-processing","numpy","pandas","python","sci","tens"],"created_at":"2024-11-14T04:41:14.648Z","updated_at":"2025-07-31T08:05:34.440Z","avatar_url":"https://github.com/TrainingByPackt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n# Data Science with Python\nData Science with Python will help you get comfortable with using the Python environment for data science. You will learn all the libraries that a data scientist uses on a daily basis. By the end of this book, you will be able to take a large raw dataset, clean it, manipulate it, and run machine learning algorithms to obtain results that influence business decisions. \n\nData Science with Python by Rohan Chopra, Aaron England and Mohamed Noordeen\n\n## What you will learn\n*\tPre-process data to make it ready to use for machine learning\n*  Create data visualizations with Matplotlib\n*\tUse scikit-learn to perform dimension reduction using principal component analysis (PCA)\n*\tSolve classification and regression problems\n*\tGet predictions using the XGBoost library\n*\tProcess images and create machine learning models to decode them\n*\tProcess human language for prediction and classification\n*\tUse TensorBoard to monitor training metrics in real time\n*\tFind the best hyperparameters for your model with AutoML\n\n\n\n### Hardware Requirements\nFor an optimal student experience, we recommend the following hardware configuration:\n* **Processor**: Intel Core i5 or equivalent\n* **Memory**: 4GB RAM (8 GB Preferred)\n* **Storage**: 15 GB available hard disk space\n* Internet connection\n\n### Software Requirements\nYou'll also need the following software installed in advance:\n* **OS**: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X\n* **Browser**: Google Chrome/Mozilla Firefox Latest Version\n* Notepad++/Sublime Text as IDE (optional, as you can practice everything using Jupyter Notebook in your browser)\n* Python 3.4+ (the latest version is Python 3.7) installed (https://python.org)\n* Anaconda (https://www.anaconda.com/distribution/)\n\n\n\n#### Please note\nYou can download the dataset for the following lessons from the respective URL:\n\nLesson 06, Lesson 07 and Lesson 08: https://drive.google.com/drive/folders/1SZ7vVby_gfb8Isu4b-fJlSJBfqDCsCVh?usp=sharing\nLesson 06 and Lesson 08 use the same dataset\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrainingbypackt%2Fdata-science-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftrainingbypackt%2Fdata-science-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrainingbypackt%2Fdata-science-with-python/lists"}