Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful.

Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow visualizations to help you see how the math behind machine learning actually works. 

We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.

Applied Learning Project

By the end of this Specialization, you will be ready to:

• Represent data as vectors and matrices and identify their properties like singularity, rank, and linear independence

• Apply common vector and matrix algebra operations like the dot product, inverse, and determinants

• Express matrix operations as linear transformations

• Apply concepts of eigenvalues and eigenvectors to machine learning problems including Principal Component Analysis (PCA)

• Optimize different types of functions commonly used in machine learning

• Perform gradient descent in neural networks with different activation and cost functions

• Identity the features of commonly used probability distributions

• Perform Exploratory Data Analysis to find, validate, and quantify patterns in a dataset

• Quantify the uncertainty of predictions made by machine learning models using confidence intervals, margin of error, p-values, and hypothesis testing.

• Apply common statistical methods like MLE and MAP