What are the two types of Machine Learning?

Machine Learning can be categorized into different types based on the kind of learning approach utilized in the model's training process, and the correct answer highlights the two primary methods: Supervised and Unsupervised Learning.

In Supervised Learning, algorithms learn from labeled data, using input-output pairs to make predictions or classifications. This method relies on a training dataset where the outcome is already known, allowing the model to learn and make informed decisions based on previously seen examples. This is crucial in applications where specific results are desired, such as predicting crop yields based on various input factors like weather, soil composition, and agricultural practices.

Unsupervised Learning, on the other hand, involves training models on data without labeled responses. The objective here is to discover hidden patterns or intrinsic structures in the input data. This method is particularly useful for clustering data into groups or segments where no prior labeling exists. It is widely used in market segmentation, anomaly detection in farming practices, and identifying relationships within the data without predefined categories.

This knowledge of the two types of Machine Learning provides a foundational understanding of how data can be leveraged in agriculture, particularly in yield monitoring and precision farming applications.