Hello there, welcome to my repository! This is a comprehensive collection of all my Machine Learning projects, which showcases my understanding and application of various Machine Learning concepts and techniques.
This repository consists of a collection of different Machine Learning projects spanning different domains and datasets. The projects are primarily written in Python, and use a variety of machine learning models, libraries, and frameworks such as scikit-learn, TensorFlow, PyTorch, Keras, and more.
- Project 3: Project Name and Short Description
This project is based on the Intel Image Classification dataset, which contains a total of 17k images of natural scenes from around the world, including both natural landscapes and buildings. The dataset has been split into 14k images for training and 3k for testing, with each image sized at 150x150 pixels.
The main challenge tackled in this project was to correctly classify these images into one of six classes: "buildings", "forest", "glacier", "mountain", "sea", and "street". To solve this multi-class classification problem, I utilized deep learning techniques such as Convolutional Neural Networks (CNNs). This allowed for robust and effective learning from the complex, high-dimensional data present in the images.
The outcomes of this project included not only the development of a predictive model with high accuracy, but also an in-depth exploration of various deep learning techniques and their application to real-world image classification tasks.
Link: (Link to the Project folder or file)
In this project, Convolutional Neural Networks (CNNs) were constructed both from scratch and using pre-defined architectures, namely the EfficientNet and ResNet families. This approach demonstrates the capacity to create custom machine learning solutions and adapt established architectures for specific tasks.
The process also involved extensive hyperparameter tuning. This step indicates proficiency in refining and optimizing machine learning models to enhance their performance and increase accuracy.
The project concluded with an in-depth evaluation of the fine-tuned model, emphasizing the role of hyperparameters in model performance and the efficacy of deep learning in handling critical classification tasks.
Link: (Link to the Project folder or file)
(Short description of the project, the techniques used, the problem solved, and the outcome)
Link: (Link to the Project folder or file)
List the dependencies of your projects here. Examples:
- Python
- Scikit-learn
- Pandas
- NumPy
- Matplotlib
- TensorFlow
- Keras
- PyTorch
Please make sure to install all the necessary dependencies before running the projects.
If you have any questions, feel free to reach out to me at Your Email or on my LinkedIn.