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This theorem is crucial in inferential statistics as it allows us to make inferences about the population parameters based on sample data. Differentiate between supervised and unsupervised learning algorithms. What is cross-validation, and why is it used in Machine Learning?
Machine Learning 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 Machine Learning: supervisedlearning, unsupervised learning, and reinforcement learning.
The dedicated Statistics module focussing on ExploratoryDataAnalysis, Probability Theory, and Inferential Statistics. You will also explore the fundamental principles of Statistics for Data Analytics, covering topics such as random numbers, variables and types, diverse graphical techniques, and various sampling methods.
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Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Explain the difference between supervised and unsupervised learning. In traditional programming, the programmer explicitly defines the rules and logic.
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