This repository contains practical experiments, tutorials, and small projects in Machine Learning (ML) and Deep Learning (DL), including PyTorch fundamentals, classical ML algorithms, and GAN experiments.

Note: This repository is continuously updated with new experiments and notebooks.

ml-dl-labs/
├── ml/ # Classical ML experiments and small projects
│ ├── 01_handling_missing_values.ipynb
│ ├── california-housing-price-prediction/
│ │ └── 01_california_housing_price_prediction.ipynb
│ └── README.md
├── dl/ # Deep Learning experiments and tutorials
│ ├── classification/ # CNN and other classification notebooks
│ ├── gans/ # Generative Adversarial Networks experiments
│ └── pytorch-fundamentals/ # Tensors, training loops, neural networks
└── .git/

• ml/
  Classical ML experiments, toy projects, and data-handling exercises.

• dl/classification/
  Deep learning classification tasks including MNIST, hand sign recognition, and fashion datasets.

• dl/gans/
  GAN-based experiments for generating synthetic data and other exercises.

• dl/pytorch-fundamentals/
  PyTorch core concepts: tensors, autograd, neural network training, and small model implementations.

1. Clone the repository:

git clone https://github.com/your-username/ml-dl-labs.git
cd ml-dl-labs

Author: Aroosh Ahmad — AI Engineer (NLP, LLMs, ML Systems)             GitHub • LinkedIn