{"id":26324679,"url":"https://github.com/adilshamim8/ml-roadmap-and-notes","last_synced_at":"2025-03-15T18:28:12.527Z","repository":{"id":274754909,"uuid":"923962742","full_name":"AdilShamim8/ML-Roadmap-and-Notes","owner":"AdilShamim8","description":"ML Roadmap and Notes A structured guide to Machine Learning, featuring notes and resources on key concepts, algorithms, and tools for learners of all levels.","archived":false,"fork":false,"pushed_at":"2025-03-14T09:04:55.000Z","size":6567,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-14T10:22:25.648Z","etag":null,"topics":["ai","artificial-intelligence","data-science","deep-learning","deep-learning-projects","machine-learning","machine-learning-algorithms","machine-learning-library","machine-learning-models","machine-learning-practice","machine-learning-projects","machine-learning-tutorials"],"latest_commit_sha":null,"homepage":"http://adilshamim.me/","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/AdilShamim8.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":"2025-01-29T06:36:35.000Z","updated_at":"2025-03-14T09:04:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"d2d2f519-4098-4ba9-a1d5-855fe52a0a9c","html_url":"https://github.com/AdilShamim8/ML-Roadmap-and-Notes","commit_stats":null,"previous_names":["adilshamim8/ml-roadmap-and-notes"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdilShamim8%2FML-Roadmap-and-Notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdilShamim8%2FML-Roadmap-and-Notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdilShamim8%2FML-Roadmap-and-Notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdilShamim8%2FML-Roadmap-and-Notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AdilShamim8","download_url":"https://codeload.github.com/AdilShamim8/ML-Roadmap-and-Notes/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243773626,"owners_count":20345872,"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":["ai","artificial-intelligence","data-science","deep-learning","deep-learning-projects","machine-learning","machine-learning-algorithms","machine-learning-library","machine-learning-models","machine-learning-practice","machine-learning-projects","machine-learning-tutorials"],"created_at":"2025-03-15T18:28:11.981Z","updated_at":"2025-03-15T18:28:12.508Z","avatar_url":"https://github.com/AdilShamim8.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ML-Roadmap-and-Notes\nA curated list of Machine learning Notes, links, projects, and datasets to help you conquer the ML landscape in 6 months\n\n## Levels of Learning\n1. ### Testing the waters\n2. ### Gaining Conceptual depth\n3. ### Learning Practical Concepts\n4. ### Diving into different domains\n5. ### Pushing it with Projects\n\n## 1. Testing the waters \n\nThis level aims to familiarize you with the ML universe. You will learn a bit about everything.\n| Sr No | Name                                                         | Link                                                         |\n| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n|   1.  | Learn Python                                                 |\n|   i.  | Basics of Python                                             | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/1.%20Learn%20Python/1.%20Basics%20of%20Python) |\n|   ii. | OOP in Python                                                | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/1.%20Learn%20Python/2.%20OOP%20in%20Python) |\n|  iii. | Advance Topics                                               | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/1.%20Learn%20Python/3.%20Advance%20Topics) |\n|  iv.  | Practice Problems                                            | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/blob/main/1.%20Testing%20the%20waters/1.%20Learn%20Python/Exercise.ipynb) |\n|       |                                                              |\n|   2.  | Learn Numpy                                                  |   \n|   i.  | Numpy Notes                                                  | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/blob/main/1.%20Testing%20the%20waters/2.%20Learn%20Numpy/Numpy.ipynb) |\n|  ii.  | Numpy Practice Problems                                      | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/blob/main/1.%20Testing%20the%20waters/2.%20Learn%20Numpy/100_Numpy_exercises.ipynb) |\n|       |                                                              |\n|   3.  | Learn Pandas                                                 |\n|   i.  | Pandas Notes                                                 | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/3.%20Learn%20Pandas) |\n|  ii.  | Pandas Practice Problems                                     | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/blob/main/1.%20Testing%20the%20waters/3.%20Learn%20Pandas/100-pandas-puzzles.ipynb) |\n|       |                                                              |\n|   4.  | Learn Data Visualization                                     |\n|   i.  | Matplotlib                                                   | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/4.%20Learn%20Data%20Visualization/Matplotlib) |\n|  ii.  | Seaborn                                                      | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/4.%20Learn%20Data%20Visualization/Seaborn) |\n|       |                                                              |\n|   5.  | Descriptive Statistics Notes                                 | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/5.%20Descriptive%20Statistics) |\n|       |                                                              |\n|   6.  | Learn Data Analysis Process                                  | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/6.%20Learn%20Data%20Analysis%20Process) |\n|       |                                                              | \n|   7.  | Learn Exploratory Data Analysis (EDA) Notes                  | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/7.%20Learn%20Exploratory%20Data%20Analysis%20(EDA)) |\n|       |                                                              |\n|   8.  | Learn Machine Learning Basics Notes                          | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/8.%20Learn%20Machine%20Learning%20Basics) |\n\n## 2. Gaining Conceptual depth \n\nThis level is designed to introduce you to the fundamental concepts and algorithms of machine learning, giving you a broad overview of the field.\n\n## Roadmap | mathematics for machine learning  | [Link](https://docs.google.com/spreadsheets/d/10spJMs0Zmv5cugfFjJVc4MudyOVjl_16Ef5z54oxqnM/edit?gid=0#gid=0)\n## Book    | mathematics for machine learning  | [Link](https://github.com/mml-book/mml-book.github.io)\n| Sr No | Name                                                         | Link                                                         |\n| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n|   1.  | Learn about tensors                                          |\n|   i.  | 1. What are Tensors?                                         | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/1.%20Learn%20about%20tensors) |\n|       |                                                              |\n|   2.  | Advance Statistics Notes                                     | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/2.%20Advance%20Statistics) |\n|       |                                                              |\n|   3.  | Probability Basics Notes                                     | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/3.%20Probability%20Basics) |\n|       |                                                              |\n|   4.  | Linear Algebra Basics Notes                                  | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/4.%20Linear%20Algebra%20Basics) |\n|       |                                                              |\n|   5.  | Basics of Calculus Notes                                     | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/5.%20Basics%20of%20Calculus) |\n|       |                                                              |\n|   6.  | Machine Learning Algorithms                                  |\n|    i. | Linear Regression Notes                                      | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Linear%20Regression) |\n|   ii. | Gradient Descent Notes                                       | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/%20Gradient%20Descent) |\n|   iii.| Logistic Regression Notes                                    | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Logistic%20Regression) |\n|   iv. | Support Vector Machines Notes                                | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Support%20Vector%20Machines) |\n|    v. | Naive Bayes Notes                                            | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Naive%20Bayes) |\n|   vi. | K Nearest Neighbors Notes                                    | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/K%20Nearest%20Neighbors) |\n|  vii. | Decision Trees Notes                                         | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Decision%20Tree) |\n|  viii.| Random Forest Notes                                          | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Random%20Forest) |\n|   ix. | Bagging Notes                                                | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Bagging) |\n|   x.  | Adaboost Notes                                               | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/AdaBoost) |\n|   xi. | Gradient Boosting Notes                                      | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Gradient%20Boosting) |\n|   xii.| Xgboost Notes                                                | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/XGBoost) |\n|  xiii.| Principle Component Analysis (PCA)  Notes                    | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/PCA) |\n|   XIV.| K_Means Clustering  Notes                                    | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/K_Means) |\n|   XV. | Hierarchical Clustering Clustering  Notes                    | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/Hierarchical%20Clustering) |\n|  XVi. | DBSCAN  Notes                                                | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/6.%20Machine%20Learning%20Algorithms/Linear%20Regression/DBSCAN%20Clustering) |\n\n### Machine Learning Metrics  | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/7.%20Machine%20Learning%20Metrics) \n### Regularization  | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/9.%20Regularization)\n\n## 3. Learn Practical Concepts\n\nThis level aims to introduce you to the practical side of machine learning. What you learn at this level would help you out there in the wild.\n\n| Sr No | Name                                                         | Link                                                         |\n| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |\n|   1.  | Data Acquisition                                             |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/1.%20Data%20Acquisition/Data%20Acquistion)\n|       |                                                              |\n|   2.  | Working with missing values                                  |\n|   i.  | Complete Case Analysis                                       | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/2.%20Working%20with%20missing%20values/1.%20Complete%20Case%20Analysis) |\n|   ii. | Handling missing numerical data                              |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/2.%20Working%20with%20missing%20values/2.%20Handling%20missing%20numerical%20data) |\n|   iii.| Handling missing categorical data                            |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/2.%20Working%20with%20missing%20values/3.%20Handling%20missing%20categorical%20data) \n|   iv. | Missing indicator                                            |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/2.%20Working%20with%20missing%20values/4.%20Missing%20indicator) |\n|   v.  | KNN Imputer                                                  |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/2.%20Working%20with%20missing%20values/5.%20KNN%20Imputer) |\n|   vi. | MICE                                                         |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/2.%20Working%20with%20missing%20values/6.%20MICE) |\n|   vii.| Kaggle Notebooks and Practice Datasets                       |[Link](https://docs.google.com/document/d/1_9Y6kxNc6QTym2Y2JGEBbnCUbE1qZWLVzVXlT2eX_FQ/edit?usp=sharing)\n|       |                                                              |\n|   3.  | Feature Scaling/Normalization                                |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/3.%20Feature%20Scaling%20and%20Normalization)\n|       |                                                              |\n|   4.  | Feature Encoding Techniques                                  |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/4.%20Feature%20Encoding%20Techniques)\n|       |                                                              |\n|   5.  | Feature Transformation                                       |\n|   i.  | Function Transformer                                         |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/5.%20Feature%20Transformation/1.%20FunctionTransformer/1.%20FunctionTransformer)\n|   ii. | Power Transformations                                        |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/5.%20Feature%20Transformation/1.%20FunctionTransformer/2.%20Power%20Transformations)\n|   iii.| Binning and Binarization                                 |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/5.%20Feature%20Transformation/1.%20FunctionTransformer/3.%20Binning%20and%20Binarization)\n|       |                                                              |\n|   6.  | Working with Pipelines                                       |\n|   i.  |  Column Transformer                                          |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/6.%20Working%20with%20Pipelines/1.%20Column%20Transformer)\n|   ii. |  Sklearn Pipelines                                           |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/6.%20Working%20with%20Pipelines/2.%20Sklearn%20Pipelines)\n|       |                                                              |\n|   7.  | Handing Time and Date data                                 |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/7.%20Handing%20Time%20and%20Date%20data/1.%20Working%20with%20time%20and%20date%20data)\n|       |                                                              |\n|   8.  | Working with Outliers                                        |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/8.%20Working%20with%20Outliers/Working%20with%20Outliers)\n|       |                                                              |\n|   9.  | Feature Construction                                         |[Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/3.%20Learn%20Practical%20Concepts/1.%20Data%20Acquisition/Data%20Acquistion/9.%20Feature%20Construction/1.%20Feature%20Construction)\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadilshamim8%2Fml-roadmap-and-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadilshamim8%2Fml-roadmap-and-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadilshamim8%2Fml-roadmap-and-notes/lists"}