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Statistics plays a crucial role in machine learning, as it provides the foundation for understanding and interpreting data, making informed decisions, and evaluating the performance of machine learning models. Here are some key statistical concepts that are important for machine learning practitioners: Descriptive Statistics: Inferential Statistics: Probability and Random Variables: Sampling and Sampling Distributions: Statistical Testing for Machine Learning: Bayesian Statistics: Evaluation Metrics in Machine Learning: Cross-Validation and Bias-Variance Tradeoff: Statistical Learning Theory: Having a strong foundation in these statistical concepts enables machine learning practitioners to make informed decisions, choose appropriate models, and assess the reliability of their findings. Continuous learning and application of statistical techniques in real-world machine learning projects enhance the practitioner’s ability to build effective models.