📁 M.Tech Subject Repository

Welcome to the M.Tech Subject Repository!
This repo is organized by subject for easy navigation and revision. Each subject has its own folder containing relevant notes, code, assignments, and resources.

---

🗂️ Folder Structure

mtech/ ├── Inferential_Statistics / ├── Machine_Learning_Algorithms / ├── Deep_Neural_Network / ├── Research_Methodology / ├── Applied_Data_Science / └── README.md (this file)

---

📚 Subject Overview

Inferential Statistics

Covers statistical inference, hypothesis testing, confidence intervals, Bayesian statistics, and regression analysis. Essential for understanding the mathematical foundations of data science.

Machine Learning Algorithms

Focuses on supervised, unsupervised, and reinforcement learning algorithms. Topics include decision trees, SVMs, clustering, and ensemble methods.

Deep Neural Network

Explores architectures, training dynamics, and applications of deep learning. Includes CNNs, RNNs, transformers, and their use cases.

Research Methodology

Provides an introduction to research design, literature review, data collection, analysis techniques, academic writing, and ethics.

Applied Data Science

Practical applications of data science in industry. Covers data preprocessing, visualization, model deployment, and real-world case studies.

---

📝 How to Use

• Clone the repository:
  git clone https://github.com/SANGRAMLEMBE/MTech.git
• Navigate to a subject:
  cd mtech/Deep_Neural_Network
• Add your notes/code:
  Place files in appropriate subject folders.
• Commit & Push:
  Regularly update your work with git add, git commit, and git push.

---

🤝 Contributing

Feel free to contribute:

• Add notes or resources to any subject.
• Fix errors or improve existing content.
• Suggest new topics or subjects via Issues.

---

Last updated: August 24, 2025

Let’s organize knowledge and ace your M.Tech journey! 🚀