🤖 AI/ML – IIIT Hyderabad

This repository contains hands-on implementations, lab exercises, and mini-projects developed as part of AI & Machine Learning coursework.
It covers fundamental and advanced ML concepts through practical Jupyter notebooks.

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📘 Contents

• Perceptron and Neural Networks
• Forward & Backward Propagation
• Gradient Descent (multiple implementations)
• Linear & Polynomial Regression
• Loss Functions
• K-Means & Hierarchical Clustering
• COVID-19 Case Analysis using Regression

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🧠 Topics Covered

• Supervised Learning
• Unsupervised Learning
• Optimization Techniques
• Neural Network Training
• Clustering Algorithms
• Regression Analysis

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📂 Repository Structure

│── Implementing_Forward_and_Backward_Propagation.ipynb
│── Training_a_Neural_Network.ipynb
│── KMeans_&_Hierarchical_Clustering.ipynb
│── Module_4_Lab_1_Perceptron.ipynb
│── Module_4_Lab_2_Gradient_Descent.ipynb
│── Module_4_Lab_3_Gradient_Descent.ipynb
│── Module_5_Lab_1_Linear_and_Polynomial_Regression.ipynb
│── Module_5_Lab_2_Loss_Functions.ipynb
│── Project_Analysis_of_Covid_19_Cases_Regression.ipynb
│── README.md

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▶️ How to Run

pip install numpy pandas matplotlib scikit-learn
jupyter notebook

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📌 Use Cases

• Academic learning and practice
• Understanding ML algorithms from scratch
• Interview and revision reference
• Mini-project experimentation

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🤝 Contributions

This repository is primarily for learning purposes. Suggestions and improvements are welcome via issues or pull requests !