Linear Algebra for Machine Learning and Data Science (0)

What you'll learn

• 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 the dot product, inverse, and determinants
• Express certain types of matrix operations as linear transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems

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There are 4 modules in this course

Newly updated for 2024!

After completing this course, learners will be able to:

• Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, linear independence, etc.
• Apply common vector and matrix algebra operations like the dot product, inverse, and determinants
• Express certain types of matrix operations as linear transformations
• Apply concepts of eigenvalues and eigenvectors to machine learning problems

Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program 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 works.

 

Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

 

This is a beginner-friendly program, with a recommended background of at least high school mathematics (functions, basic algebra). We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries), as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2: Calculus for Machine Learning and Data Science, and Course 3: Probability and Statistics for Machine Learning and Data Science, of this specialization.

 

All the information provided is based on the Linear Algebra for Machine Learning and Data Science | Coursera from DeepLearning.AI