{"id":19940742,"url":"https://github.com/kurtispykes/machine-learning","last_synced_at":"2025-05-03T15:31:28.985Z","repository":{"id":43042069,"uuid":"278029871","full_name":"kurtispykes/Machine-Learning","owner":"kurtispykes","description":"All content related to machine learning from my blog","archived":false,"fork":false,"pushed_at":"2022-03-22T09:00:04.000Z","size":409,"stargazers_count":116,"open_issues_count":0,"forks_count":28,"subscribers_count":6,"default_branch":"main","last_synced_at":"2025-04-07T17:41:57.098Z","etag":null,"topics":["machine-learning","machine-learning-algorithms","mlops","python","statistics"],"latest_commit_sha":null,"homepage":"https://kurtispykes.medium.com/","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/kurtispykes.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}},"created_at":"2020-07-08T08:11:14.000Z","updated_at":"2025-01-14T14:24:27.000Z","dependencies_parsed_at":"2022-09-06T08:50:54.655Z","dependency_job_id":null,"html_url":"https://github.com/kurtispykes/Machine-Learning","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/kurtispykes%2FMachine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kurtispykes%2FMachine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kurtispykes%2FMachine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kurtispykes%2FMachine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kurtispykes","download_url":"https://codeload.github.com/kurtispykes/Machine-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252210854,"owners_count":21712292,"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":["machine-learning","machine-learning-algorithms","mlops","python","statistics"],"created_at":"2024-11-13T00:06:44.873Z","updated_at":"2025-05-03T15:31:28.665Z","avatar_url":"https://github.com/kurtispykes.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning \n\nA collection of blog articles and code related to machine learning that I've curated. \n\nI regularly write about Python, Machine Learning, Freelancing, Productivity, and Self-Branding in my \n[Medium Blog](https://kurtispykes.medium.com). With a $5 a month commitment, you can unlock an \nunlimited access to stories on Medium. If you use my \n[sign-up](https://kurtispykes.medium.com/membership) link, I'll recieve a small commision. \nIf you're already a member, [subscribe](https://kurtispykes.medium.com/subscribe) to recieve \nmy posts directly to your inbox whenever I publish. \n\n\n## Downloading the code\n\nThe simplest way to download the code is to clone the repository with `git clone`: \n```\ngit clone https://github.com/kurtispykes/Machine-Learning.git\n```\n### The steps\n1. `git clone \u003crepo\u003e`\n2. `cd \u003crepo\u003e`\n3. `pip install virtualenv` (if you don't already have virtualenv installed)\n4. `virtualenv venv` to create your new environment (called 'venv' here)\n5. `venv/bin/activate.bat` to enter the virtual environment\n6. `pip install -r requirements.txt` to install the requirements in the current environment\n\n## Table of Contents \n### General ML Concepts\n* [The Difference Between Classification and Regression](https://towardsdatascience.com/the-difference-between-classification-and-regression-in-machine-learning-4ccdb5b18fd3)\n* [Semi-Supervised Machine Learning Explained](https://towardsdatascience.com/semi-supervised-machine-learning-explained-c1a6e1e934c7)\n* [Unsupervised Machine Learning Explained](https://towardsdatascience.com/unsupervised-machine-learning-explained-1ccc5f20ca29)\n* [Model Drift in Machine Learning](https://towardsdatascience.com/a-simple-explanation-of-mlops-e47009e245f7)\n* [Building Reproducible Machine Learning Pipelines](https://medium.datadriveninvestor.com/machine-learning-model-deployment-b1eaf7ca96cd)\n* [The Machine Learning Workflow](https://towardsdatascience.com/the-machine-learning-workflow-1d168cf93dea)\n\n### Machine Learning Algorithms From Scratch\n* [Linear Regression](https://towardsdatascience.com/algorithms-from-scratch-linear-regression-c654353d1e7c): [See Full Code](machine_learning_algorithms/linear_regression.ipynb)\n* [Logistic Regression](https://towardsdatascience.com/algorithms-from-scratch-logistic-regression-7bacdfd9738e): [See Full Code](machine_learning_algorithms/logistic_regression.ipynb)\n* [Naive Bayes](https://towardsdatascience.com/algorithms-from-scratch-naive-bayes-classifier-8006cc691493): [See Full Code](machine_learning_algorithms/naive_bayes.ipynb)\n* [K-Nearest Neighbors](https://towardsdatascience.com/algorithms-from-scratch-k-nearest-neighbors-fe19b431a57): [See Full Code](machine_learning_algorithms/k-nearest_neighbors.ipynb)\n* [Decision Tree](https://towardsdatascience.com/algorithms-from-scratch-decision-tree-1898d37b02e0): [See Full Code](machine_learning_algorithms/decision_tree.ipynb)\n* [Support Vector Machine](https://towardsdatascience.com/algorithms-from-scratch-support-vector-machine-6f5eb72fce10): [See Full Code](machine_learning_algorithms/support_vector_machine.ipynb)\n* [PCA](https://towardsdatascience.com/algorithms-from-scratch-pca-cde10b835ebc)\n\n### Feature Engineering\n* [Feature Engineering for Numerical Data](https://towardsdatascience.com/feature-engineering-for-numerical-data-e20167ec18): [See Full Code](feature_engineering/feature_engineering_numerical_data.ipynb)\n* [Oversampling and Undersampling](https://towardsdatascience.com/oversampling-and-undersampling-5e2bbaf56dcf): [See Full Code](feature_engineering/oversampling_and_undersampling.ipynb)\n* [Pandas: Combining data](https://towardsdatascience.com/pandas-combining-data-b190d793b626): [See Full Code](feature_engineering/combing_data_in_pandas.ipynb)\n* [A Peek into Missing Data With Pandas](https://towardsdatascience.com/a-peek-into-missing-data-with-pandas-2fb9e5df8bd0): [See Full Code](feature_engineering/peek_into_missing_data.ipynb)\n* [Handling Missing Data](https://towardsdatascience.com/handling-missing-data-f998715fb73f): [See Full Code](feature_engineering/handling_missing_data.ipynb)\n* [Hyperparameter Optimization with Comet](https://heartbeat.comet.ml/hyperparameter-optimization-with-comet-80c6d4b83502): [See Full Code](feature_engineering/hyperparameter_opt_with_comet.ipynb)\n\n### Feature Selection\n* [Getting Started with Feature Selection](https://towardsdatascience.com/getting-started-with-feature-selection-3ecfb4957fd4): [See Full Code](feature_selection/feature_selection_beginner.ipynb)\n\n### Data Visualization\n* [Effective Data Visualization](https://towardsdatascience.com/effective-data-visualization-ef30ae560961): [See Full Code](data_visualization/data_visualization.ipynb)\n\n### MLOps\n* [A Simple Explaination of MLOps](https://towardsdatascience.com/a-simple-explanation-of-mlops-e47009e245f7)\n* [Machine Learning in The Cloud](https://medium.datadriveninvestor.com/machine-learning-in-the-cloud-66bd25bc1a2b)\n* [Machine Learning Model Deployment](https://medium.datadriveninvestor.com/machine-learning-model-deployment-b1eaf7ca96cd)\n* [Introducing CI/CD Pipelines To Your Machine Learning Project](https://medium.com/pykes-technical-notes/introducing-ci-cd-pipelines-to-your-machine-learning-project-aa610dbeea2f?source=rss------programming-5)\n* [7 Benefits of CI/CD Pipelines](https://medium.com/pykes-technical-notes/7-benefits-of-ci-cd-pipelines-22f807e81266)\n* [Make Tracking Your Machine Learning Experiments Easy](https://heartbeat.comet.ml/make-tracking-your-machine-learning-experiments-easy-afad9b9956a)\n* [Tracking Artifacts With Comet ML](https://heartbeat.comet.ml/tracking-artifacts-with-comet-ml-66897c73a856)\n\n### Evaluation Metrics\n* [Cohen's Kappa](https://towardsdatascience.com/cohens-kappa-9786ceceab58)\n* [Comprehension of the AUC-ROC Curve](https://towardsdatascience.com/comprehension-of-the-auc-roc-curve-e876191280f9)\n* [Confusion Matrix \"Un-confused\"](https://towardsdatascience.com/confusion-matrix-un-confused-1ba98dee0d7f)\n\n### Testing\n* [Testing Machine Learning Systems: Unit Tests](https://medium.com/pykes-technical-notes/testing-machine-learning-systems-unit-tests-38696264ee04): [See Full Code](testing/)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkurtispykes%2Fmachine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkurtispykes%2Fmachine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkurtispykes%2Fmachine-learning/lists"}