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MachineLearning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data. There are three main types of MachineLearning: supervisedlearning, unsupervised learning, and reinforcement learning.
Key Takeaways MachineLearning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, datapreparation, and algorithm selection. Data quality significantly impacts model performance.
These techniques span different types of learning and provide powerful tools to solve complex real-world problems. SupervisedLearningSupervisedlearning is one of the most common types of MachineLearning, where the algorithm is trained using labelled data.
Now that we have a firm grasp on the underlying business case, we will now define a machinelearning pipeline in the context of credit models. Machinelearning in credit scoring and decisioning typically involves supervisedlearning , a type of machinelearning where the model learns from labeled data.
At the core of machinelearning, two primary learning techniques drive these innovations. These are known as supervisedlearning and unsupervised learning. Supervisedlearning and unsupervised learning differ in how they process data and extract insights.
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