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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["data-science","decision-trees","jupyter-notebook","k-means-clustering","k-nearest-neighbors","linear-regression","logistic-regression","machine-learning","machine-learning-algorithms","matplotlib","numpy","pandas","principal-component-analysis","python","random-forest","scikit-learn","seaborn","support-vector-machines"],"created_at":"2025-05-20T15:11:08.501Z","updated_at":"2026-04-10T13:31:58.200Z","avatar_url":"https://github.com/Dipto9999.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Introduction to Machine Learning\n\n## Table of Contents\n* [Tutorial](Tutorial)\n* [Linear Regression](Linear_Regression)\n* [Logistic Regression](Logistic_Regression)\n* [K-Nearest Neighbors](K-Nearest_Neighbors)\n* [K-Means Clustering](K-Means_Clustering)\n* [Decision Trees and Random Forests](Decision_Trees_and_Random_Forests)\n* [Support Vector Machines](Support_Vector_Machines)\n* [Principal Component Analysis](Principal_Component_Analysis)\n\n## Brief Description\nGiven access to FreeCodeCamp Website Resources, I have touched up on some fundamental Machine Learning Algorithms.\nI installed the \u003cb\u003eAnaconda\u003c/b\u003e Data Science Platform to implement some basic \u003cb\u003ePython scikit-learn\u003c/b\u003e library\nfunctionalities in \u003cb\u003eJupyter Notebook\u003c/b\u003e.\n\n### Import Statements\nAll the Machine Learning Algorithms covered in this Introduction import the following open-source libraries.\n_____________________________________________________________________________________________________________________________________________\n\n```python\n# Import Library for Working with Tabular Data\nimport pandas as pd\n# Import Library for Numerical Computing\nimport numpy as np\n# Import Library for Data Visualization\nimport matplotlib.pyplot as plt\n# Import Another Library for Data Visualizations\n# This makes it easier to create beautiful data visualizations using matplotlib.\nimport seaborn as sns\n\n# matplotlib visualizations will embed themselves\n# directly in our Jupyter Notebook. This will make them easier to\n# access and interpret.\n%matplotlib inline\n```\n\n### PDF Report\n\nWe are able to generate a PDF Report of the Jupyter Notebook using the following command.\n\n```\njupyter nbconvert ---to webpdf \u003cfilename\u003e.ipynb\n\n```\n\nThese can be found in the Algorithm directories\n\n_____________________________________________________________________________________________________________________________________________\n\n## Source\n\u003ci\u003eThe information in this repository is derived from a FreeCodeCamp\n\u003ca href= \"https://www.freecodecamp.org/news/a-no-code-intro-to-the-9-most-important-machine-learning-algorithms-today\"\u003eArticle\u003c/a\u003e written by Nick McCullum.\u003c/i\u003e","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipto9999%2Fml_introduction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdipto9999%2Fml_introduction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipto9999%2Fml_introduction/lists"}