🧠 ML Algorithms

Welcome to my Machine Learning & Deep Learning Repository!
This repo contains all the concepts, algorithms, and code examples I am learning and practicing in the fields of ML, DL, and NLP.

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📂 Repository Content

• Machine Learning

  • Supervised Learning (Regression, Classification)
  • Unsupervised Learning (Clustering, Dimensionality Reduction)
  • Ensemble Methods (Bagging, Boosting, Random Forests)
  • Model Evaluation & Metrics

• Deep Learning

  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN, LSTM, GRU)
  • Regularization, Dropout, Optimization Techniques

• Natural Language Processing (NLP)

  • Text Preprocessing (Tokenization, Stopwords, Lemmatization)
  • Word Embeddings (Word2Vec, GloVe, FastText)
  • Transformers & Attention Mechanisms
  • Sentiment Analysis, Text Classification

• Code Examples

  • Step-by-step implementations in Python
  • Jupyter notebooks with explanations
  • Hands-on examples for algorithms and concepts

• Notes & Resources

  • Markdown notes on key ML/DL/NLP topics
  • Cheat sheets and quick references
  • Links to useful articles and papers

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🚀 Goals of this Repository

• To track and document my ML/DL/NLP learning journey
• To provide reusable and clean implementations of important algorithms
• To serve as a reference for revision and future projects

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📖 How to Use

1. Browse through the folders by topic (ML, DL, NLP, Notes, Examples).
2. Open the Jupyter notebooks or .py files to see the implementation.
3. Read the markdown notes for concise theory and intuition.
4. Try running the code on your own dataset.

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🤝 Contributions

This is a personal learning repository, but suggestions and improvements are welcome!
If you find issues or want to add better implementations, feel free to open a pull request.

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📌 Future Plans

• Add mini-projects
• Expand NLP section with more advanced transformer models

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⭐ If you find this repository helpful, don’t forget to star it!