{"id":26017863,"url":"https://github.com/watashiwasid/machine-learning-docs","last_synced_at":"2026-04-13T04:03:03.672Z","repository":{"id":280844248,"uuid":"943308299","full_name":"WatashiwaSid/machine-learning-docs","owner":"WatashiwaSid","description":"Materials I collected and or prepared while studying Machine Learning in 6th semester.","archived":false,"fork":false,"pushed_at":"2025-03-05T15:53:33.000Z","size":0,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-05T16:38:08.848Z","etag":null,"topics":["artificial-intelligence","deep-learning","machine-learning","neural-networks","pandas","scikit-learn"],"latest_commit_sha":null,"homepage":"https://www.linkedin.com/in/comicsid","language":"Python","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/WatashiwaSid.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":"2025-03-05T13:55:56.000Z","updated_at":"2025-03-05T16:04:41.000Z","dependencies_parsed_at":"2025-03-05T16:38:12.006Z","dependency_job_id":"78ab519c-47eb-42df-8184-ceb6f75621e4","html_url":"https://github.com/WatashiwaSid/machine-learning-docs","commit_stats":null,"previous_names":["watashiwasid/machine-learning-docs"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WatashiwaSid%2Fmachine-learning-docs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WatashiwaSid%2Fmachine-learning-docs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WatashiwaSid%2Fmachine-learning-docs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WatashiwaSid%2Fmachine-learning-docs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WatashiwaSid","download_url":"https://codeload.github.com/WatashiwaSid/machine-learning-docs/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242154773,"owners_count":20080733,"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":["artificial-intelligence","deep-learning","machine-learning","neural-networks","pandas","scikit-learn"],"created_at":"2025-03-06T05:30:15.199Z","updated_at":"2025-12-31T00:51:14.775Z","avatar_url":"https://github.com/WatashiwaSid.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"![Black Simple Inspirational Quotes New LinkedIn Banner (1)](https://github.com/user-attachments/assets/f43fe96c-b932-4cdb-b882-d088cdf82b48)\n# INTRODUCTION TO AI/ML\n- ML is a subset of AI.\n- AI is the meta domain that encompasses several specialized domains of learning designed to address specific tasks and challenges.  \n- This includes Machine Learning, Deep Learning, Generative AI, Computer Vision, Expert Systems, Neural Networks, Natural Language Processing, Robotics and more.\n- The focus of study in this repo is Machine Learning, and Deep Learning to some extent.\n\n# INTRODUCTION TO ML \n- Machine Learning is a subset of Artificial Intelligence that involves studying and developing statistical models and algorithms that enable machines to make decisions based on trained data.\n- At a broad level, ML algorithms can be classified into three categories:\n  - \u003cb\u003eSupervised Learning\u003c/b\u003e\n  - \u003cb\u003eUnsupervised Learning\u003c/b\u003e\n  - \u003cb\u003eReinforcement Learning\u003c/b\u003e\n\n### Supervised Learning\n- Supervised Learning deals with problems when the problem statement clearly defines what needs to be done. (predicted)\n- Supervised models learn with labelled data.\n- The variable or outcome that needs to be predicted is known as the **predictor** **variable** or **dependent variable** **(y)**.\n- The other fields in the dataset are called **features** or **independent variables** **(x)**.\n![image](https://github.com/user-attachments/assets/52118706-b7b3-45ac-ab5c-f2e2d282f971)\n\n- Applications of Supervised Learning Algorithms:\n  - Customer Churn Prediction (Classification)\n  - Bank Credit Card Fraud Detection (Classification)\n  - Email/SMS Spam Detection (Classification)\n  - Regression Problems\n\n ### Unsupervised Learning\n - Unsupervised learning problems do not have a predictor variable.\n - Unsupervised models learn exclusively through unlabelled data. (It could be weak or semi-supervised as well).\n - Applications of Unsupervised Learning Algorithms:\n   - Clustering Algorithms\n   - Principal Component Analysis\n   - Recommendation Systems\n\n   ![Task-guidance](https://github.com/user-attachments/assets/02df1112-7aa7-49f7-a359-5839ce2a1cf3)\n\n\n### Reinforcement Learning\n- Teaches AI systems to make decisions through trial and error.\n- Applicable in game systems, such as chess or other strategic games.\n- For Instance:\n  We beat the game bot using **x strategic steps** in an online game such as chess. The game could not be beaten with the same **x strategic steps** again, since the game evolves through reinforcement learning.\n\n# Workflow of a ML Problem\n\n![image](https://github.com/user-attachments/assets/e394cc04-d8d7-457f-9d3d-9801bde1f2a7)\n_Please note that **accuracy** is not the recommended metric for the evaluation of performance of a machine learning model._\n\n## Moving Forward\n| Topic      | Concepts | Python Libraries | \n| ----------- | ----------- | ----------- |\n| [classification](https://github.com/WatashiwaSid/machine-learning-docs/tree/main/classification) | Classification Algorithms | Pandas, Scikit Learn | \n| [regression](https://github.com/WatashiwaSid/machine-learning-docs/tree/main/regression) | regression, gradient descent | Scikit Learn, Numpy |\n| [clustering](https://github.com/WatashiwaSid/machine-learning-docs/tree/main/clustering) | hard and soft clustering | Scikit Learn, Seaborn |\n| [dimensionality reduction](https://github.com/WatashiwaSid/machine-learning-docs/tree/main/dimension-reduction) | pca, low variance, high corelation | Scikit Learn, Seaborn |\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwatashiwasid%2Fmachine-learning-docs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwatashiwasid%2Fmachine-learning-docs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwatashiwasid%2Fmachine-learning-docs/lists"}