AI/ML Engineer | Deep Learning & Python

I am a Machine Learning & Deep Learning Developer specializing in building AI-powered applications and predictive models. My expertise spans from designing and training neural networks to deploying interactive apps using Python and modern ML frameworks. I focus on creating efficient, accurate, and scalable solutions for image classification, regression, and real-world data problems.

Features of My Models:

• Neural Networks (ANNs & CNNs): Designing and implementing fully connected networks and convolutional networks for various classification and prediction tasks.

• Dataset Management & Dataloaders: Efficient data preprocessing, augmentation, and batching for training deep learning models.

• Transfer Learning: Leveraging pre-trained models to improve performance and reduce training time.

• Model Training & Evaluation: Using TensorFlow and PyTorch for model training, validation, and accuracy optimization.

• Deep Learning Applications: Real vs fake face detection, Fashion MNIST classification, clock model predictions, and more.

• Deployment & Interactive Apps: Streamlit-based web apps for real-time model inference and visualization.

• Visualization & Analysis Tools: Matplotlib, Seaborn for data insights and performance tracking.

Tools & Technologies:

• Programming & Data: Python, Pandas, NumPy

• Machine Learning & Deep Learning: TensorFlow, PyTorch, Scikit-learn, Keras

• Data Visualization: Matplotlib, Seaborn

• Web & App Deployment: Streamlit

• Image & Data Handling: PIL (Python Imaging Library), OpenCV

• Model Management & Utilities: TensorFlow Hub, Torchvision, Pretrained Models / Transfer Learning