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Luis Serrano, Elena Sanina, Anshuman Singh, and Magdalena Bouza

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.

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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.

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What's inside

Three courses

Linear Algebra for Machine Learning and Data Science

After completing this course, learners will be able to represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.

Calculus for Machine Learning and Data Science

After completing this course, learners will be able to: • Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients • Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods

Probability & Statistics for Machine Learning & Data Science

(33 hours)
Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. After completing this course, learners will be able to describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions.

Learning objectives

  • A deep understanding of the math that makes machine learning algorithms work.
  • Statistical techniques that empower you to get more out of your data analysis.

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