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Linear Algebra for Machine Learning and Data Science
  • Level Foundation
  • Duration 22 hours
  • Course by DeepLearning.AI
  • Offered by

About

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. • 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 Mathematics for Machine Learning and Data science is a foundational online program created in 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 actually 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. We also recommend a basic familiarity with Python, 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 of this specialization, Calculus for Machine Learning and Data Science now, and Course 3, Probability and Statistics for Machine Learning and Data Science when it is released in April.

Modules

Specialization & Course Introduction

    • 4 Videos
    • 3 Readings
    • 1 ExternalTool
Show info about module content

1 External Tool

  • Intake Survey

4 Videos

  • Specialization introduction
  • Course introduction
  • What to expect and how to succeed
  • A note on programming experience

3 Readings

  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!
  • Notations
  • Learning Python: Recommended Resources

Systems of Equations

    • 9 Videos
    • 4 Readings
    • 3 Quiz
    • 2 Lab
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2 Labs

  • Introduction to NumPy Arrays
  • Linear Systems as Matrices

9 Videos

  • Linear Algebra Applied I
  • Linear Algebra Applied II
  • System of sentences
  • System of equations
  • System of equations as lines and planes
  • A geometric notion of singularity
  • Singular vs non-singular matrices
  • Linear dependence and independence
  • The determinant

4 Readings

  • Check your knowledge
  • Interactive Tool: Graphical Representation of Linear Systems with 2 variables
  • Interactive Tool: System of Equations as Planes (3x3)
  • (Optional) Downloading your Notebook and Refreshing your Workspace

3 Quiz

  • Practice Quiz 1
  • Practice Quiz 2
  • Graded quiz

Week 1 Wrap Up

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Conclusion

1 Readings

  • Week 1 - Slides

Solving systems of linear equations: Elimination

    • 5 Videos
    • 2 Readings
    • 1 Quiz
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5 Videos

  • Solving non-singular system of linear equations
  • Solving singular system of linear equations
  • Solving system of equations with more variables
  • Matrix row-reduction
  • Row operations that preserve singularity

2 Readings

  • Check your knowledge
  • Interactive Tool: Graphical Representation of Linear Systems with 3 variables

1 Quiz

  • Practice Quiz

Ungraded lab

    • 1 Lab
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1 Labs

  • Introduction to the Numpy.linalg sub-library

Solving system of linear equations: Row echelon form and rank

    • 6 Videos
    • 1 Quiz
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6 Videos

  • The rank of a matrix
  • The rank of a matrix in general
  • Row echelon form
  • Row echelon form in general
  • Reduced row echelon form
  • The Gaussian Elimination Algorithm

1 Quiz

  • Graded Quiz

Programming Assignment: Gaussian Elimination

    • 2 Readings
    • 1 Programming
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1 Programming

  • Gaussian Elimination

2 Readings

  • (Optional) Assignment Troubleshooting Tips
  • (Optional) Partial Grading for Assignments

Week 2 Wrap Up

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Conclusion

1 Readings

  • Week 2 - Slides

Vector algebra

    • 6 Videos
    • 1 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Practice Quiz

1 Labs

  • Vector Operations: Scalar Multiplication, Sum and Dot Product of Vectors

6 Videos

  • Machine Learning Motivation
  • Vectors and their properties
  • Vector operations
  • The dot product
  • Geometric Dot Product
  • Multiplying a matrix by a vector

1 Readings

  • Check your knowledge

Linear transformations

    • 7 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

7 Videos

  • Matrices as linear transformations
  • Linear transformations as matrices
  • Matrix multiplication
  • The identity matrix
  • Matrix inverse
  • Which matrices have an inverse?
  • Neural networks and matrices

1 Readings

  • Interactive Tool: Linear Transformations

1 Quiz

  • Graded Quiz

Ungraded Labs

    • 2 Lab
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2 Labs

  • Matrix Multiplication
  • Linear Transformations

Programming Assignment: Single Perceptron Neural Networks for Linear Regression

    • 1 Programming
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1 Programming

  • Linear Transformations and Neural Networks

Week 3 Wrap Up

    • 1 Videos
    • 1 Readings
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1 Videos

  • Conclusion

1 Readings

  • Week 3 - Slides

Determinants In-depth

    • 5 Videos
    • 1 Readings
    • 1 Quiz
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5 Videos

  • Week 4 Introduction
  • Singularity and rank of linear transformations
  • Determinant as an area
  • Determinant of a product
  • Determinants of inverses

1 Readings

  • Check your knowledge

1 Quiz

  • Practice Quiz

Eigenvalues and Eigenvectors

    • 14 Videos
    • 1 Readings
    • 1 Quiz
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14 Videos

  • Bases in Linear Algebra
  • Span in Linear Algebra
  • Eigenbases
  • Eigenvalues and Eigenvectors
  • Calculating Eigenvalues and Eigenvectors
  • On the Number of Eigenvectors
  • Dimensionality Reduction and Projection
  • Motivating PCA
  • Variance and Covariance
  • Covariance Matrix
  • PCA - Overview
  • PCA - Why It Works
  • PCA - Mathematical Formulation
  • Discrete Dynamical Systems

1 Readings

  • Interactive Tool: Linear Span

1 Quiz

  • Graded Quiz

Programming Assignment: Eigenvalues and Eigenvectors

    • 1 Lab
    • 1 Programming
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1 Programming

  • Application of Eigenvalues and Eigenvectors: Webpage navigation model and PCA

1 Labs

  • Interpreting Eigenvalues and Eigenvectors

Week 4 Wrap Up

    • 1 Videos
    • 2 Readings
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1 Videos

  • Conclusion

2 Readings

  • Week 4 - Slides
  • How is your course experience so far?

Course Resources

    • 3 Readings
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3 Readings

  • Reading: Textbooks and resources
  • References
  • Acknowledgments

Auto Summary

Embark on a foundational journey in Data Science & AI with "Linear Algebra for Machine Learning and Data Science," taught by Luis Serrano from DeepLearning.AI on Coursera. This beginner-friendly course demystifies essential mathematical tools for machine learning, focusing on vectors, matrices, eigenvalues, and linear transformations. Ideal for those with a high school math background and basic Python knowledge, you'll gain practical skills through hands-on labs using Python and Jupyter Notebooks. The course is part of a comprehensive specialization, with flexible subscription options and a total duration of 1320 minutes, tailored for aspiring data scientists and machine learning engineers.

Luis Serrano
Instructor

Luis Serrano