Types of ML and the ML life cycle

Depending on how models learn, ML can be classified into three types:

• Supervised learning, where each data sample must have a label indicating the correct outcome. The model learns from labeled structured data, such as CSV files, by adjusting its internal parameters based on its error when it guesses the result. Supervised learning is by far the most used type of learning in classification images, voice and language recognition, numerical forecasting, and more.
• Unsupervised learning, on the other hand, involves training on data, usually unstructured, without labels. Unsupervised learning uses clustering and other techniques to understand the underlying structure of data, identify patterns, and perform anomaly detection, fraud detection, social network analysis, market segmentation, and supervised learning.
• Reinforcement learning relies on an agent to behave in an environment and learn by performing certain actions, observing the results...