Probability and Statistics for Machine Learning and Data Science

Updated for 2024! Mathematics for Machine Learning and Data Science is an online foundational program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply mathematical concepts through programming. And in this specialization, you will apply the mathematical concepts you learn using Python programming in hands-on labs. You will need basic to intermediate Python programming skills to succeed. After completing this course, you will be able to: - Describe and quantify the uncertainty inherent in the predictions made by machine learning models using concepts of probability, random variables, and probability distributions. - Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science, such as Bernoulli, binomial, and Gaussian distributions - Apply common statistical methods, such as maximum likelihood estimation (MLE) and maximum prior estimation (MAP), to machine learning problems - Evaluate the performance of machine learning models using interval estimates and errors - Apply concepts from statistical hypothesis testing to commonly used tests in data science, such as AB testing - Perform exploratory data analysis on a dataset to find, validate, and quantify patterns Many machine learning and data science engineers and practitioners need help learning math, and even experienced practitioners can feel at a loss due to a lack of math skills. This specialization uses innovative math pedagogy to help you learn quickly and intuitively, with courses that use simple visualizations to help you see how the math behind machine learning actually works. We recommend that you have a high school level of math (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditionals, debugging). Assignments and labs are written in Python, but the course introduces all the machine learning libraries you’ll use.