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Foundations of Probability in Python
4.8(202)16k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate Python course delivers the mathematical and computational foundation of probability necessary for machine learning and data science. Moving from fundamental random variables and conditional probability to core statistical theorems, it equips learners to model uncertainty programmatically in Python.
Who It's For
Data science students, machine learning engineers, and Python analysts seeking to master the probabilistic principles behind statistical models.
Key Takeaways
- Random Variables & Distributions: Calculate expected value, variance, and model continuous and discrete probability distributions in Python.
- Conditional Probability & Bayes: Compute conditional probabilities to evaluate likelihoods under changing event constraints.
- Core Limit Theorems: Explore the Law of Large Numbers and Central Limit Theorem through Python simulations.
About this course
Probability is the study of regularities that emerge in the outcomes of random experiments. In this course, you'll learn about fundamental probability concepts like random variables (starting with the classic coin flip example) and how to calculate mean and variance, probability distributions, and conditional probability. We'll also explore two very important results in probability: the law of large numbers and the central limit theorem. Since probability is at the core of data science and machine learning, these concepts will help you understand and apply models more robustly. Chances are everywhere, and the study of probability will change the way you see the world. Let’s get random!
Price
Free
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