Machine Learning Notes & Study Material

Welcome to the Machine Learning Notes & Study Material repository!
This repository is curated for learners and practitioners who want clear, organized, and practical resources on Machine Learning, including notes, study guides, and hands-on examples.

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🗂️ Repository Structure

This repository is structured by core topics in machine learning, each with its own directory:

• Cost Function, Loss Function & Gradient Descent
• Cross Validation
• Data Cleaning & Preprocessing
• Data Collection & Wrangling
• Feature Engineering
• Machine Learning Model
• Model Evolution
• Model Parameters
• Train & Test Data Split
• Machine Learning Goals.xlsx — A spreadsheet for tracking ML learning objectives
• LICENSE — Repository license
• README.md — This file

You can browse each folder for detailed notes, code, and explanations.

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📚 What’s Inside?

• Topic-wise Notes:
  Each directory contains summaries, definitions, and explanations of key concepts.
• Practical Guides:
  Step-by-step instructions for implementing ML techniques.
• Code Examples & Datasets:
  Example scripts and sample data to help reinforce learning.
• Study & Revision Material:
  Tips, best practices, and checklists for exams or interviews.

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✨ Who Is This For?

• Students studying ML or preparing for exams/interviews
• Self-learners building foundational knowledge
• Educators seeking teaching material
• Anyone interested in clear, concise ML notes and practical examples

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🚀 How To Use

1. Clone the repo:

   git clone https://github.com/DevSharma03/Machine_Learning.git

2. Explore by topic:
   Browse the folders for notes, guides, and sample code.
3. Use the spreadsheet:
   Track your learning progress with Machine Learning Goals.xlsx.
4. Practice and experiment:
   Apply methods and concepts in your projects using the provided resources.

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🤝 Contributing

Contributions are welcome!
If you want to add notes, code, or corrections, please submit a pull request.

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📬 Contact

For questions or feedback, open an issue.

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📄 License

This repository is licensed under the MIT License.
Feel free to use, share, or adapt the material, with proper attribution.

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Happy Learning!
If you find this repository helpful, please star it on GitHub!