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Math 0-1: Probability for Data Science & Machine Learning (Udemy.com)

A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers

4.8

167 reviews

Created by: Lazy Programmer Team

Last updated February 2026

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

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

Rating4.8 out of 5higher than 94% of them
Reviews167#364 of 462 by number of reviews
Length23.6 hourslonger than 86% of them
Last updatedFebruary 2026more recently than 59% of them

Course Description

Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.

Either you never studied this math, or you studied it so long ago you've forgotten it all.

What do you do?

Well my friends, that is why I created this course.

Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").

Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).

Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.

In short, probability cannot be avoided!

If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.

This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.

Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with a deep understanding of probability so that you can apply it correctly and effectively in data science, machine learning, and beyond.

Are you ready?

Let's go!

Suggested prerequisites:

Differential calculus, integral calculus, and vector calculus
Linear algebra
General comfort with university/collegelevel mathematics

All Levels

Time: 23.6h

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Instructor Details

Lazy Programmer Team

4.8 Rating
167 Reviews

The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.

Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into academia led him to pursue computer engineering, with a specialized focus on machine learning and pattern recognition. Undeterred by boundaries, he then ventured into the realm of statistics, exploring its applications in financial engineering.

Recognized as a trailblazer in his field, the Lazy Programmer quickly embraced the power of deep learning when it was still in its infancy. As one of the pioneers, he fearlessly embarked on instructing one of the first-ever online courses on deep learning, catapulting him to the forefront of the industry.