If you're having trouble viewing the Jupyter Notebook directly on GitHub, you can easily access and explore the content by using the nbviewer service at https://nbviewer.org/. Simply copy and paste the URL of the notebook (e.g., https://github.com/smrrazavian/my_notes_and_learnig_procces/blob/main/machine-learning/supervised_learning/decision_trees.ipynb) into nbviewer, and it will render the notebook correctly for easy reading and interaction.
Welcome to the Programming Knowledge Repository! This repository is dedicated to sharing valuable resources related to programming, with a special focus on Machine Learning and Deep Learning concepts. Here, we aim to build a collaborative space where programmers, developers, and learners can come together to access, contribute, and learn from a diverse collection of materials.
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Supervised Learning
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Unsupervised Learning
- Neural Network Basics
- Activation Functions
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Generative Adversarial Networks (GAN)
- Transfer Learning
- Natural Language Processing (NLP) with Deep Learning
- Perceptrons and Multi-Layer Perceptrons
- Backpropagation
- Optimization Algorithms
- Regularization Techniques
- Hyperparameter Tuning
- Sorting Algorithms
- Searching Algorithms
- Graph Algorithms
- Dynamic Programming
- Greedy Algorithms
- Divide and Conquer
- Arrays and Linked Lists
- Stacks and Queues
- Trees and Binary Search Trees
- Hash Tables
- Heaps
- Graph Data Structures
- Python Basics and Syntax
- Object-Oriented Programming (OOP) in Python
- File Handling and Input/Output (I/O)
- Python Libraries for Data Science (e.g., NumPy, Pandas)
- Web Scraping with Python
Contributions are greatly appreciated! If you have any valuable resources, such as books, notes, lectures, or video links related to programming and Machine Learning/Deep Learning, you are welcome to share them with the community.
To contribute, follow these steps:
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Fork the repository to your GitHub account.
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Create a new branch with a descriptive name for your contribution.
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Add your content to the appropriate category folder (e.g.,
books,notes,lectures,videos). -
Commit your changes and create a pull request.
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Once reviewed, your contribution will be merged and credited.
Please ensure that your contributions comply with the Code of Conduct for a respectful and inclusive community environment.
This repository is open-source and available under the MIT License. By contributing to this repository, you agree to license your contributions under the same terms.
If you have any questions, suggestions, or need support regarding this repository or its content, feel free to create an issue or reach out to the maintainers.
Let's learn and grow together!
Happy coding!