Prerequisites: Familiarity with basic programming concepts (preferably Python) and introductory mathematical concepts (algebra). Prior exposure to fundamental probability terms is helpful but not strictly required.

Level: Intermediate

What You'll Learn

• Probability Fundamentals

  Apply core probability concepts like conditional probability, Bayes' theorem, and random variables to data scenarios.

• Probability Distributions

  Identify and utilize common probability distributions (Normal, Poisson, Binomial, etc.) relevant to modeling data in machine learning.

• Descriptive Statistics

  Calculate and interpret measures of central tendency, dispersion, and correlation to summarize datasets.

• Statistical Inference

  Understand sampling methods, the Central Limit Theorem, and construct confidence intervals for population parameters.

• Hypothesis Testing

  Formulate and conduct hypothesis tests (like t-tests) to make data-driven decisions and evaluate models.

• Regression Analysis

  Grasp the fundamentals of linear regression, fit models to data, and evaluate their performance.

• Practical Implementation

  Implement statistical techniques and probability calculations using Python libraries such as NumPy, SciPy, and Pandas.