Which aspect does the term 'machine learning' NOT typically include?

The term 'machine learning' encompasses a variety of methodologies and techniques designed to enable systems to learn from data and improve over time without being explicitly programmed for each task. Supervised learning, unsupervised learning, and reinforcement learning are all core components of machine learning.

Supervised learning strategies involve training models on labeled datasets, where the input data is paired with corresponding output labels. This allows the model to make predictions or classifications based on the labeled examples it has learned from.

Unsupervised learning techniques focus on finding patterns and structures in data that do not have labeled outcomes. Here, the system learns to group or cluster data based on the inherent characteristics present within the dataset, enabling insights that are not specifically programmed.

Reinforcement learning is another approach where models learn by interacting with an environment, receiving feedback in the form of rewards or penalties based on their actions. This method drives the learning process through trial and error, allowing systems to develop strategies for decision-making.

In contrast, the manual coding of algorithms does not embody the essence of machine learning. Rather than leveraging data to optimize performance automatically, manual coding requires explicit instructions and rules defined by human programmers. This approach lacks the adaptive learning ability that is fundamental to the methodologies encompassed within machine learning. Therefore