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Machine Learning and Deep Learning Project Portfolio

Welcome to my collection of Machine Learning (ML) and Deep Learning (DL) projects! 🚀
Here, you will find practical implementations, hands-on tutorials, and real-world use cases.

📚 Machine Learning with PyTorch and Scikit-Learn

This section contains all the source code from the book:

"Machine Learning with PyTorch and Scikit-Learn".

It is an excellent resource for understanding machine learning concepts and their implementations using:

  • PyTorch
  • Scikit-Learn

Dive in and explore real-world ML techniques with clean, reusable code.

🤖 Local RAG-Based Chatbot

A Retrieval-Augmented Generation (RAG) based chatbot app! 💬

Features:

  • Upload a PDF document.
  • Ask any questions related to the document.
  • Get accurate responses extracted from the uploaded document.

Purpose:

Enhances question-answering systems by combining retrieval and generation capabilities.

🧠 Neural Networks from Scratch

This project demystifies the inner workings of Neural Networks.

  • Learn to implement a neural network from scratch.
  • Understand its building blocks, activation functions, and forward/backward propagation.

This is ideal for beginners looking to dive deeper into AI fundamentals.

🛠️ Basics of Neural Networks

An introductory project to implement basic Neural Networks.

What You’ll Learn:

  • Key neural network concepts.
  • Step-by-step implementation of a simple NN.

Great starting point for exploring deep learning frameworks.

🕵️‍♂️ Credit Card Fraud Detection

A Classification-based Machine Learning project.

Objective:

Predict if a credit card transaction is fraudulent or not.

Technologies:

  • Machine Learning models for binary classification.
  • Data preprocessing, feature scaling, and model evaluation.

Use Case:

Improve fraud detection systems in financial sectors. 🚨

🏠 Bengaluru House Price Prediction

A House Price Prediction project for Bengaluru City! 📊

Objective:

Develop a machine learning model to predict house prices based on features like: - Location - Area - Bedrooms - Amenities

Technologies:

  • Regression models for price prediction.
  • Exploratory Data Analysis (EDA) and feature engineering.

This project is perfect for learning regression techniques and tackling real estate datasets.

🚀 Let’s Build Together

Feel free to explore these projects, clone repositories, and contribute!
Whether you're a beginner or an expert, these projects will guide you in understanding core ML and DL concepts through practical coding.

Happy Learning! 🧑‍💻✨