Introduction
Normalization is a crucial data preprocessing technique in the field of data science and machine learning. It involves transforming numerical data into a standard scale, making it easier for algorithms to converge during training and ensuring that no particular feature dominates due to its larger magnitude. Here's an overview of normalization techniques commonly used in data science:

Min-Max Scaling (Normalization):

This method scales the data between a specified range, typically 0 and 1.
The formula for Min-Max Scaling is given by:

Z-score Normalization (Standardization):

Z-score normalization transforms the data to have a mean of 0 and a standard deviation of 1.
The formula for Z-score normalization is given by:

Robust Scaling:

Robust scaling is similar to Min-Max Scaling but is less sensitive to outliers.
It uses the interquartile range (IQR) to scale the data, making it more robust against extreme values.
Log Transformation:

Log transformation is useful for handling data with a wide range of values or data that follows an exponential distribution.
It helps to stabilize variance and make the data more normal.
Normalization in Neural Networks:

In the context of neural networks, normalization is often applied to the input data or intermediate layers using techniques like Batch Normalization or Layer Normalization.
Batch Normalization normalizes the activations of a neural network layer by adjusting and scaling the outputs during training.
Sparse Data Normalization:

For sparse data, like text data represented as a bag-of-words, techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) normalization are commonly used.

Choosing the appropriate normalization technique depends on the nature of the data and the requirements of the machine learning algorithm. It's important to experiment with different methods to find the one that works best for your specific use case.