Machine Learning Portfolio

Welcome to my Machine Learning Portfolio. This repository showcases various projects I've worked on, demonstrating different aspects of machine learning, deep learning, and data analysis.

Projects

1. Generate Handwritten Digit Images using DCGAN

Description: Implemented a Deep Convolutional Generative Adversarial Network (DCGAN) to generate realistic handwritten digit images similar to those in the MNIST dataset.

Key Features:

• Data preprocessing and normalization of the MNIST dataset.
• Design and training of the generator and discriminator networks.
• Visualization of generated digit images over training epochs.

Technologies Used: Python, TensorFlow/Keras, NumPy, Matplotlib

Usage: Navigate to the Generate-handwritten-digit-images-DCGAN directory and run the Jupyter notebook to train the model and generate images.

2. IMDB Reviews Sentiment Analysis using LSTM

Description: Developed a Long Short-Term Memory (LSTM) network to perform sentiment analysis on IMDB movie reviews, classifying them as positive or negative.

Key Features:

• Text preprocessing including tokenization and padding.
• Embedding layer for word representations.
• Construction and training of the LSTM model.
• Evaluation of model performance on test data.

Technologies Used: Python, TensorFlow/Keras, Natural Language Toolkit (NLTK)

Usage: Navigate to the IMDB-reviews-Sentiment-Analysis-LSTM directory and execute the Jupyter notebook to preprocess data, train the model, and evaluate results.

3. Fashion MNIST End-to-End Project

Description: An end-to-end project involving the classification of fashion items using the Fashion MNIST dataset.

Key Features:

• Data exploration and visualization.
• Building and training a Convolutional Neural Network (CNN).
• Model evaluation and hyperparameter tuning.

Technologies Used: Python, TensorFlow/Keras, Matplotlib

Usage: Navigate to the fashion-mnist-end-to-end-project directory and run the provided scripts to train and evaluate the model.

4. Plant Disease Prediction using CNN

Description: Developed a Convolutional Neural Network to identify plant diseases from images, aiding in early detection and treatment.

Key Features:

• Image data augmentation and preprocessing.
• Design and training of the CNN model.
• Evaluation of model accuracy and deployment considerations.

Technologies Used: Python, TensorFlow/Keras, OpenCV

Usage: Navigate to the plant-disease-prediction-cnn-deep-learning-project directory and follow the instructions in the notebook to train the model and make predictions.

5. Breast Cancer Prediction using PyTorch Neural Network

Description: Implemented a neural network using PyTorch to predict breast cancer occurrence based on medical data.

Key Features:

• Data loading and preprocessing.
• Building and training a feedforward neural network.
• Model evaluation using accuracy and loss metrics.

Technologies Used: Python, PyTorch, Pandas, Scikit-learn

Usage: Navigate to the pytorch-neural-network-breast-cancer-prediction directory and execute the Python scripts to train and test the model.

6. Text Summarizer

Description: Created a text summarization tool that generates concise summaries from longer textual documents.

Key Features:

• Text preprocessing and tokenization.
• Implementation of extractive summarization techniques.
• Evaluation of summary quality using ROUGE metrics.

Technologies Used: Python, Natural Language Toolkit (NLTK), Gensim

Usage: Navigate to the text-summarizer directory and run the Jupyter notebook to input text and generate summaries.

Getting Started

To explore any of these projects:

1. Clone the repository:

   git clone https://github.com/kvamsi7/ML-portfolio.git