One-hot encoding is a method used to convert categorical variables into a binary matrix, where each category is represented by a unique binary vector. This is useful when dealing with categorical data that does not have an inherent order. Label encoding, on the other hand, assigns a unique integer to each category, which is useful for ordinal data where the categories have a specific order. Binary encoding is a more compact form of encoding where categories are converted into binary code.

Target encoding involves encoding categorical variables using the target variable, which can help improve the model's performance. Frequency encoding assigns values to categories based on their frequency of occurrence in the dataset, making it useful for handling high cardinality categorical features. Embedding encoding is commonly used in natural language processing (NLP) to represent words or phrases in a continuous vector space, capturing semantic relationships between them.

Data normalization and data standardization are techniques used to scale numerical data to a standard range, making it easier for machine learning algorithms to process. Discretization involves converting continuous data into discrete bins, which can simplify the model and improve performance. Data preprocessing is the broader process of preparing raw data for analysis, and it includes steps like data cleaning, encoding, and scaling. Feature engineering is the process of creating new features from existing data to improve the performance of machine learning models.

All these techniques are part of the broader field of data preprocessing, which is essential for ensuring that machine learning models receive high-quality, relevant data for training. Proper data encoding and preprocessing can significantly enhance the accuracy and efficiency of machine learning algorithms.