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Mathematics for Machine Learning and Data Science Specialization

What you'll learn

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.

Join Free: Mathematics for Machine Learning and Data Science Specialization

Specialization - 3 course series

Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning.

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 plugins and visualizations to help you see how the math behind machine learning actually works. 

This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science.

Applied Learning Project

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

Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence

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

Express certain types of matrix operations as linear transformations 

Apply concepts of eigenvalues and eigenvectors to machine learning problems

Optimize different types of functions commonly used in machine learning

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

Describe and quantify the uncertainty inherent in predictions made by machine learning models

Understand the properties of commonly used probability distributions in machine learning and data science

Apply common statistical methods like MLE and MAP

Assess the performance of machine learning models using interval estimates and margin of errors 

Apply concepts of statistical hypothesis testing

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