Statistics and probability provide the core framework for understanding uncertainty, data distributions, and model performance in machine learning. This section introduces key concepts such as random variables, probability distributions, statistical measures, and inference, showing how they are applied to data analysis, model evaluation, and decision-making under uncertainty.
Descriptive statistics summarize and describe the key features of a dataset, providing a foundation for data analysis in machine learning. This section covers measures of central tendency, dispersion, and data distribution, helping to identify patterns, detect anomalies, and inform preprocessing and modeling decisions.
Probability fundamentals provide the framework for reasoning under uncertainty in machine learning. This section introduces key concepts such as random variables, probability distributions, conditional probability, and Bayes’ theorem, which are essential for modeling uncertainty, making predictions, and designing probabilistic algorithms.
Probability distributions describe how data values are spread and are essential for modeling and inference in machine learning. This section covers common distributions—such as normal, binomial, and uniform—and explains their role in understanding data, estimating probabilities, and building probabilistic models.
