Gain the foundational knowledge of probability and statistics necessary for machine learning applications. This course covers essential distributions, statistical inference, hypothesis testing, and regression analysis, providing practical implementation examples using Python libraries.
Prerequisites Basic Python required.
Level:
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.