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