There are several types of data used in machine learning, including:

1. Numerical data: This type of data includes values that are represented as numbers, such as age, height, weight, etc.

2. Categorical data: This type of data includes values that are represented as categories or labels, such as colors, types of animals, or political affiliations.

3. Text data: This type of data includes textual information such as product reviews, tweets, news articles, etc.

4. Image data: This type of data includes images or videos, such as those used in computer vision applications.

5. Time-series data: This type of data includes values that are recorded over time, such as stock prices, weather patterns, or sensor data.

To prepare data for use in machine learning, several steps need to be taken. These steps include:

1. Data collection: This involves gathering the data from various sources, such as databases, APIs, or web scraping.

2. Data cleaning: This involves removing any irrelevant, redundant, or erroneous data from the dataset. This may involve tasks such as data normalization, feature selection, or outlier detection.

3. Data preprocessing: This involves transforming the data into a format that can be used by the machine learning algorithms. This may involve tasks such as data encoding, scaling, or imputation.

4. Data splitting: This involves dividing the dataset into training, validation, and testing sets. The training set is used to train the model, the validation set is used to tune the model parameters, and the testing set is used to evaluate the model's performance.

5. Data augmentation: This involves generating additional training data by applying transformations to the existing data, such as rotations, translations, or flips. This can help to improve the model's robustness and generalization ability.

Overall, preparing data for use in machine learning is a crucial step in the machine learning pipeline and requires careful attention to detail to ensure accurate and reliable results.