Data Transformation in AI

Data Transformation

• Data transformation is a crucial step in machine learning, as it enables accurate predictions.
• There are various types of data transformation, depending on the type of data and the desired outcome.

Types of Data Transformation

• Data Cleaning: removing incorrect or incomplete information, handling missing values, and dealing with outliers or extreme values.
• Feature Extraction: reducing a large amount of information to a smaller set of more useful variables, using techniques like Principal Component Analysis (PCA) or t-SNE.
• Feature Creation: adding extra information to the dataset, making use of data that would otherwise be ignored, and improving the accuracy of predictions.
• Data Normalization: making sure all values in the dataset are on the same scale, often used with numerical data.
• Data Aggregation: combining multiple datasets into one, often used when working with data from different sources.
• Data Disaggregation: splitting one large dataset into several smaller ones, often used to split aggregated data into smaller datasets.

Data Normalization Techniques

• Min-Max Normalization: scaling values to a range between 0 and 1 by subtracting the minimum value and dividing by the range of the feature.
• Z-Score Normalization: scaling values to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.

Unsupervised Learning

• Examples: discovering market segments, grouping customers into different market segments, and grouping news articles into sets of articles about the same story.

Machine Learning Approaches

• Supervised Learning: involves training a model on labeled data to make predictions.
• Unsupervised Learning: involves training a model on unlabeled data to discover patterns or relationships.
• Reinforcement Learning: involves training an agent to make a sequence of decisions by interacting with an environment.
• Hybrid Approaches: combining supervised and unsupervised learning, such as semi-supervised learning and reinforcement learning.

ML Life Cycle

• Data: the heart of every machine learning algorithm, comes in various shapes and sizes, and must be transformed before use in a machine learning project.