This course provides a practical and theoretical tour of the most essential probability distributions that are most often used for modern machine learning and data science. We will explore the fundamental building blocks for modeling discrete events (Bernoulli, binomial, multinomial distributions) and continuous quantities (Gaussian distribution) and discuss the implications of Bayes Theorem. Moreover, we will discuss two perspectives in estimating the model parameters, namely Bayesian perspective and frequentist perspective and learn how to reason about uncertainty in model parameters themselves using the powerful beta and Dirichlet distributions for Bayesian perspective and maximum likelihood estimate for frequentist perspective. By the end of this course, you will have a fluent command of the mathematical "language" needed to understand, build, and interpret probabilistic models.
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Foundations for Machine Learning
This course is part of Practical Machine Learning: Foundations to Neural Networks Specialization
Instructor: Peter Chin
Included with
Recommended experience
What you'll learn
How to model data with key distributions, apply Bayes and MLE, and quantify uncertainty via conjugate priors.
Skills you'll gain
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November 2025
26 assignments
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There are 8 modules in this course
What's included
1 video2 readings
What's included
3 videos2 readings4 assignments2 ungraded labs
What's included
4 videos1 reading5 assignments1 ungraded lab
What's included
4 videos2 readings5 assignments3 ungraded labs
What's included
3 videos1 reading4 assignments2 ungraded labs
What's included
1 video2 readings2 assignments3 ungraded labs
What's included
6 videos1 reading5 assignments3 ungraded labs
What's included
1 reading1 assignment
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Dartmouth College
Dartmouth College
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