Math 0-1: Probability for Data Science & Machine Learning
A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers
4.90 (149 reviews)
1 375
students
23.5 hours
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May 2025
last update
$74.99
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What you will learn
Conditional probability, Independence, and Bayes' Rule
Use of Venn diagrams and probability trees to visualize probability problems
Discrete random variables and distributions: Bernoulli, categorical, binomial, geometric, Poisson
Continuous random variables and distributions: uniform, exponential, normal (Gaussian), Laplace, Gamma, Beta
Cumulative distribution functions (CDFs), probability mass functions (PMFs), probability density functions (PDFs)
Joint, marginal, and conditional distributions
Multivariate distributions, random vectors
Functions of random variables, sums of random variables, convolution
Expected values, expectation, mean, and variance
Skewness, kurtosis, and moments
Covariance and correlation, covariance matrix, correlation matrix
Moment generating functions (MGF) and characteristic functions
Key inequalities like Markov, Chebyshev, Cauchy-Schwartz, Jensen
Convergence in probability, convergence in distribution, almost sure convergence
Law of large numbers and the Central Limit Theorem (CLT)
Applications of probability in machine learning, data science, and reinforcement learning
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6110451
udemy ID
05/08/2024
course created date
23/11/2024
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