Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Become a top polymath
·
Module 6
Probability and Statistics for AI
1
Probability Rules: Addition, Multiplication, and Complement
Apply the fundamental rules of probability (addition, multiplication, complement)
2
Conditional Probability and Bayes' Theorem
Compute conditional probabilities and apply Bayes' theorem to update beliefs
3
Discrete vs. Continuous Random Variables
Distinguish between discrete and continuous random variables and their distributions
4
Measures of Central Tendency and Dispersion
Compute and interpret mean, variance, and standard deviation for a dataset
5
Understanding the Normal Distribution
Describe the properties and significance of the normal distribution
6
Central Limit Theorem and Sampling Distributions
Explain the Central Limit Theorem and the concept of a sampling distribution
Hypothesis Testing Fundamentals
Formulate and perform a basic hypothesis test, interpreting the p-value and significance level
8
Random Samples and Summary Statistics in Python
Generate random samples and compute summary statistics in Python
Previous module
Calculus for Machine Learning
Next module
Foundations of Machine Learning