Probability & Statistics for Machine Learning & Data Science
Completed by Lawrence O'Neill Godfrey
August 31, 2024
33 hours (approximately)
Lawrence O'Neill Godfrey's account is verified. Coursera certifies their successful completion of Probability & Statistics for Machine Learning & Data Science
What you will learn
Describe and quantify the uncertainty inherent in predictions made by machine learning models
Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science
Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems
Assess the performance of machine learning models using interval estimates and margin of errors
Skills you will gain
- Category: Box Plots
- Category: Sampling (Statistics)
- Category: Probability
- Category: Statistical Hypothesis Testing
- Category: A/B Testing
- Category: Bayesian Statistics
- Category: Statistics
- Category: Statistical Visualization
- Category: Statistical Machine Learning
- Category: Statistical Methods
- Category: Probability & Statistics
- Category: Probability Distribution
