{"id":26418603,"url":"https://github.com/coder-sasha/ml-tutorial","last_synced_at":"2026-05-17T12:15:32.494Z","repository":{"id":282640955,"uuid":"949220706","full_name":"coder-sasha/ML-Tutorial","owner":"coder-sasha","description":"Machine Learning code examples and texts: 2019-2023","archived":false,"fork":false,"pushed_at":"2025-03-16T01:19:56.000Z","size":2534,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-16T01:20:41.606Z","etag":null,"topics":["cnn","lstm","ml","nltk-python","python3","spacy-nlp"],"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/coder-sasha.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":"2025-03-16T00:07:26.000Z","updated_at":"2025-03-16T01:19:59.000Z","dependencies_parsed_at":"2025-03-16T01:32:29.980Z","dependency_job_id":null,"html_url":"https://github.com/coder-sasha/ML-Tutorial","commit_stats":null,"previous_names":["coder-sasha/ml-tutorial"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder-sasha%2FML-Tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder-sasha%2FML-Tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder-sasha%2FML-Tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder-sasha%2FML-Tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/coder-sasha","download_url":"https://codeload.github.com/coder-sasha/ML-Tutorial/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244141576,"owners_count":20404835,"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":["cnn","lstm","ml","nltk-python","python3","spacy-nlp"],"created_at":"2025-03-18T01:48:46.410Z","updated_at":"2026-05-17T12:15:32.447Z","avatar_url":"https://github.com/coder-sasha.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"**Machine Learning (ML) and Artificial Intelligence (AI) have become phrases of the decade.**  \r\nThe goal of these tutorials is to give you an introduction to the basic concepts and mechanics of creating machine learning systems.  \r\nThe scripts and notebooks will walk you through working examples of machine learning code so that you can see the blueprint and begin to create your own working machine learning programs.  \r\nWe will explore decision trees, an approach to machine learning that produces logical rules from data, then turn to neural networks and finally explore a practical application of ML tools and methods.  \r\n\r\n**What Exactly Is Machine Learning?**  \r\nMachine learning is just a different way of computer programming.  \r\nIn traditional programming, we tell the computer what to do.  \r\nIn machine learning, instead of giving the computer a program or formulas we describe what output we want,  \r\nexemplified an input, and the machine (or rather the program) creates data procesing rules that should give us desired results:  \r\n\r\n\t\t\tTraditional Programming:  \r\n\t\t\tRules + Data → Output\r\n   \r\n\t\t\tMachine Learning:\r\n\t\t\tDesired Output + Data → Rules  \r\n\t\t\t\r\nThis mini-tutorial offers Python notebooks scripts. Scripts are run as a regular Python programs, notebooks (file format is .ipynb) are run from your web browser.  \r\nThe advantage of notebooks over regular Python scripts is that an interactive Python notebook integrates code, documentation, and the result of running the code all in one place.  \r\n\r\nThe recommended sequence of study is:  \r\n•\tbegin with **work_with_python** and **work_with_pandas** then follow to **nlp**;  \r\n•\tstart to wotk with Neural Networks  in **simple_nn**;  \r\n•\tproceed with **text_classification_with_nn**, **nn_for_mktdata**;  \r\n•\tfinish by opening **work_with_spacy**;  \r\n\r\n**Have Fun!**\r\n \r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoder-sasha%2Fml-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcoder-sasha%2Fml-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoder-sasha%2Fml-tutorial/lists"}