Course Outline

Introduction to Linear Algebra

Why You Should Improve Your Linear Algebra Knowledge for Machine Learning

Learning Linear Algebra Notations

Understanding Vectors

  • Vector Properties and Characteristics
  • Performing Vector Operations

Understanding Matrices

  • Matrix Properties and Characteristics
  • Performing Matrix Operations and Transformations
  • Working with Special Matrices

Solving Linear Systems

  • Representing Problems as Linear Systems
  • Solving Linear Systems

Linear Mappings with Matrices

  • Orthogonal Matrices
  • The Gram-Schmidt Process

Reflecting and Manipulating Images with Matrices

Understanding Eigenvalues and Eigenvectors and their Application to Data Problems

Examining Google's PageRank Algorithm with Eigenvalues and Eigenvectors

Understanding Principal Components Analysis (PCA) for Machine Learning

Understanding Linear Regression for Machine Learning

Project: Solving a Machine Learning Problem with Linear Algebra

Summary and Conclusion

Requirements

  • Basic experience or familiarity with machine learning
  • Basic programming experience
  14 Hours
 

Testimonials

Related Courses

AdaBoost Python for Machine Learning

  14 hours

Artificial Intelligence (AI) with H2O

  14 hours

AutoML with Auto-Keras

  14 hours

AutoML

  14 hours

Google Cloud AutoML

  7 hours

AutoML with Auto-sklearn

  14 hours

Pattern Recognition

  21 hours

DataRobot

  7 hours

Data Mining with Weka

  14 hours

H2O AutoML

  14 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

Pattern Matching

  14 hours

Machine Learning with Random Forest

  14 hours

RapidMiner for Machine Learning and Predictive Analytics

  14 hours

Apache SystemML for Machine Learning

  14 hours