📄️ Basic Concepts
Introduction to the fundamental pillars of statistics in ML: Populations vs. Samples, Descriptive vs. Inferential statistics, and Data Types.
📄️ Descriptive Statistics
Mastering measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation, range) to summarize and understand data distributions.
📄️ Data Visualization
Exploring the essential plots and charts used in statistical analysis to identify patterns, distributions, and outliers in Machine Learning datasets.
📄️ Inferential Statistics
Understanding how to make predictions and inferences about populations using samples, hypothesis testing, and p-values.