A hands-on collection of machine learning and deep learning projects built while studying AI, ML, and Data Science.
This repository documents my learning journey through machine learning and deep learning, covering both structured and unstructured data problems.
Projects span classical ML with Scikit-Learn to deep learning with TensorFlow/Keras, with a focus on practical implementation, model evaluation, and iterative improvement.
| Project | Type | Description |
|---|---|---|
| Heart Disease Prediction | Binary Classification | Predicts the presence of heart disease using structured medical data |
| Bluebook for Bulldozers | Regression | Predicts bulldozer sale prices from historical auction data |
| Project | Type | Description |
|---|---|---|
| Dog Vision | Multi-class Classification | Identifies dog breeds from images using TensorFlow and transfer learning (MobileNetV2) |
- Machine Learning & Deep Learning fundamentals
- Computer Vision
- Binary, Multi-class Classification & Regression
- Transfer Learning
- Data Preprocessing & Feature Engineering
- Model Evaluation & Metrics
| Category | Tools |
|---|---|
| Language | Python |
| Deep Learning | TensorFlow / Keras, MobileNetV2 |
| Classical ML | Scikit-Learn |
| Data | Pandas, NumPy |
| Visualization | Matplotlib |
| Environment | Jupyter Notebook |
Some projects follow course-guided implementations; others include additional experimentation, personal modifications, and model improvements made during the learning process.
For project-specific details, models, and results, explore the individual project folders.