Statistics is used to process complex problems in the real world so that Data Scientists and Analysts can look for meaningful trends and changes. This course helps you learn about some statistical analyzing tools and its accurate application. Key concepts include probability distributions, statistical significance, hypothesis testing, and regression. Furthermore, machine learning requires understanding Bayesian thinking.
Skills covered
Advanced Statistics
Hypothesis testing
Type-I and Type-II error
Course Syllabus
Advanced Statistics for Machine Learning
Foundations of statistics
Concept of standard error and probability
Rules of probability
Normal distribution
Hands-on exercise on normality distribution
Building confidence interval and sample size problem
Null and alternate hypothesis
Type I and type II error
Concept of P-value
Hypothesis formulation exercises
T-test application- One sample problem and solution