https://github.com/adilshamim8/ml-roadmap-and-notes
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.
https://github.com/adilshamim8/ml-roadmap-and-notes
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
Last synced: 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/adilshamim8/ml-roadmap-and-notes
- Owner: AdilShamim8
- Created: 2025-01-29T06:36:35.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-14T09:04:55.000Z (2 months ago)
- Last Synced: 2025-03-14T10:22:25.648Z (2 months ago)
- 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
- Language: Jupyter Notebook
- Homepage: http://adilshamim.me/
- Size: 6.26 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ML-Roadmap-and-Notes
A curated list of Machine learning Notes, links, projects, and datasets to help you conquer the ML landscape in 6 months
## Levels of Learning
1. ### Testing the waters
2. ### Gaining Conceptual depth
3. ### Learning Practical Concepts
4. ### Diving into different domains
5. ### Pushing it with Projects
## 1. Testing the waters
This level aims to familiarize you with the ML universe. You will learn a bit about everything.
| Sr No | Name | Link |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1. | Learn Python |
| 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) |
| 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) |
| iii. | Advance Topics | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/1.%20Learn%20Python/3.%20Advance%20Topics) |
| iv. | Practice Problems | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/blob/main/1.%20Testing%20the%20waters/1.%20Learn%20Python/Exercise.ipynb) |
| | |
| 2. | Learn Numpy |
| i. | Numpy Notes | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/blob/main/1.%20Testing%20the%20waters/2.%20Learn%20Numpy/Numpy.ipynb) |
| 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) |
| | |
| 3. | Learn Pandas |
| i. | Pandas Notes | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/3.%20Learn%20Pandas) |
| 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) |
| | |
| 4. | Learn Data Visualization |
| i. | Matplotlib | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/4.%20Learn%20Data%20Visualization/Matplotlib) |
| ii. | Seaborn | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/4.%20Learn%20Data%20Visualization/Seaborn) |
| | |
| 5. | Descriptive Statistics Notes | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/1.%20Testing%20the%20waters/5.%20Descriptive%20Statistics) |
| | |
| 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) |
| | |
| 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)) |
| | |
| 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) |
## 2. Gaining Conceptual depth
This level is designed to introduce you to the fundamental concepts and algorithms of machine learning, giving you a broad overview of the field.
## Roadmap | mathematics for machine learning | [Link](https://docs.google.com/spreadsheets/d/10spJMs0Zmv5cugfFjJVc4MudyOVjl_16Ef5z54oxqnM/edit?gid=0#gid=0)
## Book | mathematics for machine learning | [Link](https://github.com/mml-book/mml-book.github.io)
| Sr No | Name | Link |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 1. | Learn about tensors |
| i. | 1. What are Tensors? | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/1.%20Learn%20about%20tensors) |
| | |
| 2. | Advance Statistics Notes | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/2.%20Advance%20Statistics) |
| | |
| 3. | Probability Basics Notes | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/3.%20Probability%20Basics) |
| | |
| 4. | Linear Algebra Basics Notes | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/4.%20Linear%20Algebra%20Basics) |
| | |
| 5. | Basics of Calculus Notes | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/5.%20Basics%20of%20Calculus) |
| | |
| 6. | Machine Learning Algorithms |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
### Machine Learning Metrics | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/7.%20Machine%20Learning%20Metrics)
### Regularization | [Link](https://github.com/AdilShamim8/ML-Roadmap-and-Notes/tree/main/2.%20Gaining%20Conceptual%20depth/9.%20Regularization)
## 3. Learn Practical Concepts
This 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.
| Sr No | Name | Link |
| ----- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 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)
| | |
| 2. | Working with missing values |
| 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) |
| 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) |
| 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)
| 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) |
| 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) |
| 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) |
| vii.| Kaggle Notebooks and Practice Datasets |[Link](https://docs.google.com/document/d/1_9Y6kxNc6QTym2Y2JGEBbnCUbE1qZWLVzVXlT2eX_FQ/edit?usp=sharing)
| | |
| 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)
| | |
| 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)
| | |
| 5. | Feature Transformation |
| 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)
| 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)
| 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)
| | |
| 6. | Working with Pipelines |
| 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)
| 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)
| | |
| 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)
| | |
| 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)
| | |
| 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)