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https://github.com/youssefhosni/practical-machine-learning
Practical machine learning notebook & articles covers the machine learning end to end life cycle.
https://github.com/youssefhosni/practical-machine-learning
data-science machine-learning
Last synced: about 6 hours ago
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Practical machine learning notebook & articles covers the machine learning end to end life cycle.
- Host: GitHub
- URL: https://github.com/youssefhosni/practical-machine-learning
- Owner: youssefHosni
- Created: 2022-09-14T19:30:28.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-16T18:19:32.000Z (about 1 year ago)
- Last Synced: 2024-12-16T05:03:40.177Z (7 days ago)
- Topics: data-science, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 5.09 MB
- Stars: 923
- Watchers: 19
- Forks: 201
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
Awesome Lists containing this project
README
# Practical Machine Learning
Practical machine learning notebook & articles cover the machine learning life cycle.
[](https://github.com/youssefHosni/Practical-Machine-Learning/blob/master/LICENSE)
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## Overview of Machine Learning Project Life Cycle ##
* [End-to-End Machine Learning Workflow [Part 1]](https://medium.com/mlearning-ai/end-to-end-machine-learning-workflow-part-1-b5aa2e3d30e2?sk=2c0fa63e0cd3e09bc9329c1f20c63f1f)
* [End-to-End Machine Learning Workflow [Part 2]](https://medium.com/mlearning-ai/end-to-end-machine-learning-workflow-part-2-e7b6d3fb1d53?sk=06cde2cb868ac46a1dd1e71064b76b05)
## Setting Performance Baseline & Success Metrics
* [How Data Science is Helping Businesses Stay Ahead of the Game: 9 Inspiring Use Cases](https://medium.com/geekculture/how-data-science-is-helping-businesses-stay-ahead-of-the-game-9-inspiring-use-cases-9eba7b14c262?sk=f918caf0f682f797b9f6b2f734cf7d73)
* [How to Set Performance Baseline for Your Machine Learning Project Effectively?](https://pub.towardsai.net/how-to-set-performance-baseline-for-your-machine-learning-project-effectively-5be7cffdd68d?sk=51fe014a4f7c7831b2163e4b972477f1)
## Data Collection
## Data Preprocessing & Feature Engineering
* [How To Split Data Effectively for Your Data Science Project](https://pub.towardsai.net/how-to-split-the-data-effectively-for-your-data-science-project-a9cb6a387b70?sk=7036bbef95e24baeaa2f1a98afa33491) [[Code](https://github.com/youssefHosni/Machine-Learning-Practical-Guide/blob/main/How%20To%20Split%20The%20Data%20Effectively%20for%20Your%20Data%20Science%20Project.ipynb) | [Article](https://pub.towardsai.net/how-to-split-the-data-effectively-for-your-data-science-project-a9cb6a387b70?sk=7036bbef95e24baeaa2f1a98afa33491) | [Kaggle Notebook](https://www.kaggle.com/code/youssef19/how-to-split-the-data-effectively)]
* [Six Reasons Why Your Model Gives Bad Results](https://medium.com/mlearning-ai/six-reasons-why-your-model-give-bad-results-db2804f0da0e?sk=144ae1fe14011ae3a7eb5e8bc0d1f599)
## Modeling
* [Brief Guide for Machine Learning Model Selection](https://medium.com/mlearning-ai/brief-guide-for-machine-learning-model-selection-a19a82f8bdcd?sk=f3fe7b646cfbc1b8818e6cd4a61814e5)
### Supervised Machine Learning Modeling
* [Practical Guide to Support Vector Machine in Python](https://pub.towardsai.net/practical-guide-to-support-vector-machines-in-python-dc0e628d50bc?sk=3736c436ed9ec33011b453d852f53746) [[Code](https://github.com/youssefHosni/Machine-Learning-Practical-Guide/blob/main/Practical%20Guide%20to%20Support%20Vector%20Machines%20in%20Python%20.ipynb) | [Article](https://pub.towardsai.net/practical-guide-to-support-vector-machines-in-python-dc0e628d50bc?sk=3736c436ed9ec33011b453d852f53746)]
* [Practical Guide to Boosting Algorithms In Machine Learning](https://pub.towardsai.net/practical-guide-to-boosting-algorithms-in-machine-learning-61c023107e12?sk=4924d002b480475afec71c900ab3b469) [[Code]() | [Article](https://pub.towardsai.net/practical-guide-to-boosting-algorithms-in-machine-learning-61c023107e12?sk=4924d002b480475afec71c900ab3b469)]
### Unsupervised Machine Learning Modeling
* [Overview of Unsupervised Machine Learning Tasks & Applications](https://pub.towardsai.net/overview-of-unsupervised-machine-learning-tasks-applications-139db2239e2c?sk=26aa82893548ddc3c2916d4ee3c91d65)
* [Practical Guide to Dimesnioality Reduction in Python]() [[Code](https://github.com/youssefHosni/Practical-Guide-to-ML-DL-Concepts/blob/main/practical-guide-to-dimesnioality-reduction-in-pyth.ipynb) | [Article](https://medium.com/mlearning-ai/practical-guide-to-dimesnioality-reduction-in-python-9da6c84ad8ee?sk=ba37d536c5b52d79d7df19064639d4a4)]
* [How to Find the Optimal Number of Clusters Effectively]() [ [Code](https://github.com/youssefHosni/Machine-Learning-Practical-Guide/blob/main/How%20to%20Find%20the%20Optimal%20Number%20of%20Clusters%20Effectively.ipynb) | [Article](https://pub.towardsai.net/stop-using-elbow-diagram-to-find-best-k-value-and-use-this-instead-568b13d77561?sk=d9456c70a04d6d5b020da45dcad5024f) | [Kaggle Notebook](https://www.kaggle.com/code/youssef19/finding-the-optimal-number-of-clusters-effectively) ]
### Deep Learning Modeling
* [Maximizing the Impact of Data Augmentation: Effective Techniques and Best Practices](https://pub.towardsai.net/maximizing-the-impact-of-data-augmentation-effective-techniques-and-best-practices-c4cad9cd16e4?sk=c91290c8d4d69ad8df051818262ad015)
* [Building Complex Models Using Keras Functional API](https://pub.towardsai.net/building-complex-deep-learning-models-using-keras-functional-api-38090f4769a4?sk=85e11759a720c074c7bab9cc1b5d1d06) [[Code](https://github.com/youssefHosni/Machine-Learning-Practical-Guide/blob/main/Building_Complex_Deep_Learning_Models_Using_Keras_Functional_API.ipynb) | [Article](https://pub.towardsai.net/building-complex-deep-learning-models-using-keras-functional-api-38090f4769a4?sk=85e11759a720c074c7bab9cc1b5d1d06) | [Kaggle Notebook](https://www.kaggle.com/code/youssef19/building-complex-network-with-keras-functional-api)]
* [A Quick Setup for Neural Networks Hyperparameters for Best Results](https://pub.towardsai.net/a-quick-setup-for-neural-networks-hyperparameters-for-best-results-3a5a446abb3a?sk=9c9f6bf03b6895dcd0112a34158a2785)
* [Building A Recurrent Neural Network From Scratch In Python](https://pub.towardsai.net/building-a-recurrent-neural-network-from-scratch-in-python-3ad244b1054f?sk=3fcfd18bbb18fd280826c64b547f130e)
## Model Evaluation
[Why Should You Not Completely Trust In Test Accuracy?](https://pub.towardsai.net/why-should-you-not-completely-trust-in-test-accuracy-b4a80398c599?sk=6e369f1328757e79052f8b389cb2adb5)
## Machine Learning Explainability
* [Machine Learning Models Are No Longer A Black Box](https://medium.com/mlearning-ai/4-methods-to-unbox-the-machine-learning-models-black-box-8358a8bce3a6?sk=7c3f175a08a3f521b1cc77e9e9e429a3)
## MLOps & Model Deployment
* Step-by-Step Guide on Deploying Yolo3 Model on Fast API [Article](https://pub.towardsai.net/step-by-step-guide-on-deploying-yolo-model-on-fast-api-fcc6b60f5c26?sk=3ec77d08f4ff915cadcda7f0f474fc0b) | [Code]()
* [Common Machine Learning Deployment Patterns & Their Applications](https://pub.towardsai.net/common-machine-learning-deployment-patterns-their-applications-84ae9afc5b37?sk=5364822167bd9012ab360498572caf9a)
* [Key Challenges of Machine Learning Model Deployment](https://pub.towardsai.net/key-challenges-of-machine-learning-model-deployment-c48768d0e7c8?sk=5823a710321aa7122af5454c4eb4073a)
* [From Detection to Correction: How to Keep Your Production Data Clean and Reliable](https://pub.towardsai.net/from-detection-to-correction-how-to-keep-your-production-data-clean-and-reliable-6dddb72c3ab5?sk=4aee6335d3a5478b08af0b4f49d4fc99)
* [A Comprehensive Introduction to Machine Learning Experiment Tracking](https://pub.towardsai.net/a-comprehensive-introduction-to-machine-learning-experiment-tracking-3ef2cfb2c783?sk=bd961a1c0984266d195bdcb49e356545)