This is my portfolio of Machine and Deep Learning projects. Each project includes a brief overview, the tools and technologies used, and the outcomes achieved. All of the code and relevant datasets are available in the corresponding project repository.
- Project 1: Chest X-ray detector using VGG16
- Project 2: Comparision of 4 CNN models on 2 Datasets
- Project 3: Face Mask Detector using Transfer learning and Mobilenet v2
This is a deep learning project where I have trained a convolutional neural network (CNN) to classify chest X-ray images into two categories: COVID-positive and COVID-negative. The model was built using TensorFlow and Keras and achieved an accuracy of 98% on the test set.
This is a Kaggle notebook where I have experimented with four different convolutional neural network (CNN) models for a classic binary classification problem: cats vs. dogs.
This is a Kaggle notebook where I've leveraged three different models, a Deep Neural Network (DNN) model published in OpenCV's GitHub that can detect faces, and a basic Convolutional Neural Network (CNN) and MobileNet v2, which are trained on a dataset of masked and unmasked people.
The projects in this repository use the following dependencies:
- Python 3.x
- TensorFlow 2.x
- OpenCV
- NumPy
- Matplotlib
- Keras
You can install these dependencies using pip.
pip install tensorflow opencv-python numpy matplotlib
Each project is contained in its own directory, and includes a README file with detailed instructions on how to run the project and use its functionalities.
This Machine and Deep Learning Portfolio showcases my skills and expertise in various aspects of machine learning, such as data preprocessing, exploratory data analysis, feature engineering, model selection, and evaluation. I have implemented various projects using popular libraries such as TensorFlow and OpenCV, and have included detailed instructions on how to use and run each project.