diff --git a/docs/source/piqture.data_loader.rst b/docs/source/piqture.data_loader.rst
index b87b980..5cdb4ae 100644
--- a/docs/source/piqture.data_loader.rst
+++ b/docs/source/piqture.data_loader.rst
@@ -1,10 +1,125 @@
 Data Loader
 ============================
 
-piqture.data\_loader.mnist\_data\_loader module
+`piqture.data_loader.mnist_data_loader` module
 -----------------------------------------------
 
+This module provides a `load_mnist_dataset` function that simplifies loading the MNIST dataset for machine learning and deep learning experiments. It supports custom batch sizes, label selection, image resizing, and normalization options.
+
 .. automodule:: piqture.data_loader.mnist_data_loader
    :members:
    :undoc-members:
    :show-inheritance:
+
+Overview
+--------
+
+The `load_mnist_dataset` function in this module is designed to streamline the process of loading and preparing the MNIST dataset for image-based machine learning models, especially those involving quantum machine learning or custom image processing workflows.
+
+Features
+--------
+
+- Supports custom image resizing to specified dimensions.
+- Optionally filters specific labels from the MNIST dataset.
+- Integrates custom normalization using `MinMaxNormalization`.
+- Provides separate training and testing DataLoaders.
+
+.. note::
+
+   Make sure that the `torch` and `torchvision` libraries are installed, as these are used internally for dataset handling and transformations.
+
+Function Documentation
+----------------------
+
+**`load_mnist_dataset`**
+
+.. autofunction:: piqture.data_loader.mnist_data_loader.load_mnist_dataset
+
+Usage Example
+-------------
+
+Here's an example of how to use the `load_mnist_dataset` function to load the MNIST dataset and apply custom configurations:
+
+.. code-block:: python
+
+    from piqture.data_loader import mnist_data_loader
+
+    # Load MNIST dataset with custom configurations
+    train_loader, test_loader = mnist_data_loader.load_mnist_dataset(
+        img_size=(32, 32),          # Resize images to 32x32
+        batch_size=64,              # Set batch size to 64
+        labels=[0, 1, 2],           # Include only labels 0, 1, and 2
+        normalize_min=0.0,          # Normalize minimum value to 0.0
+        normalize_max=1.0           # Normalize maximum value to 1.0
+    )
+
+    # Print some batch information
+    for images, labels in train_loader:
+        print(f"Batch image shape: {images.shape}")
+        print(f"Batch labels: {labels}")
+        break
+
+Parameters
+----------
+
+- **`img_size`** (*int* or *tuple[int, int]*, optional):
+  - Size to which MNIST images will be resized.
+  - If an integer, images will be resized to a square of that size.
+  - If a tuple, it should specify `(height, width)` for the images.
+  - **Default:** `28` (images are resized to 28x28 pixels).
+
+- **`batch_size`** (*int*, optional):
+  - Specifies the number of samples per batch for training and testing DataLoaders.
+  - If not specified, the batch size defaults to `1`.
+
+- **`labels`** (*list[int]*, optional):
+  - A list of integers representing the labels to include in the dataset.
+  - For example, setting `labels=[0, 1]` will include images of digits 0 and 1 only.
+
+- **`normalize_min`** (*float*, optional):
+  - Minimum value for pixel normalization.
+  - **Default:** `None` (no normalization).
+
+- **`normalize_max`** (*float*, optional):
+  - Maximum value for pixel normalization.
+  - **Default:** `None` (no normalization).
+
+Returns
+-------
+
+- **`Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader]`**:
+  - A tuple containing:
+    - **Training DataLoader**: A PyTorch DataLoader for training data.
+    - **Testing DataLoader**: A PyTorch DataLoader for testing data.
+
+Related Functions and Classes
+-----------------------------
+
+**`collate_fn`**
+
+The `collate_fn` function is used to filter and organize batches based on the specified `labels`. It only includes images that match the desired labels.
+
+.. autofunction:: piqture.data_loader.mnist_data_loader.collate_fn
+
+Dependencies
+------------
+
+- **torch**: Required for creating PyTorch DataLoaders.
+- **torchvision**: Required for dataset loading and transformations.
+- **piqture.transforms.MinMaxNormalization**: Custom normalization transform available in the `piqture.transforms` module.
+
+Handling Edge Cases
+-------------------
+
+The function performs type checking and validation to ensure that the input parameters are valid:
+
+- **`img_size`**: Raises a `TypeError` if the value is not of type `int` or `tuple[int, int]`.
+- **`batch_size`**: Raises a `TypeError` if the value is not an integer.
+- **`labels`**: Raises a `TypeError` if the value is not a list.
+
+Refer to the `source code <https://github.com/SaashaJoshi/piqture>`_ for additional implementation details and advanced configurations.
+
+.. seealso::
+
+   - `torchvision.datasets.MNIST <https://pytorch.org/vision/stable/datasets.html#mnist>`_
+   - `piqture.transforms.MinMaxNormalization <https://piqture.readthedocs.io/en/latest/transforms.html#piqture.transforms.MinMaxNormalization>`_