- Statistical Machine Learning
- A/B Testing
- Exploratory Data Analysis
- Sampling (Statistics)
- Statistical Visualization
- Statistical Inference
- Statistical Hypothesis Testing
- Statistical Analysis
- Statistical Modeling
- Bayesian Statistics
- Descriptive Statistics
- Data Science
Probability & Statistics for Machine Learning & Data Science
Completed by Sridevi Ganapathy
April 13, 2024
33 hours (approximately)
Sridevi Ganapathy'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
