Linear Algebra for Machine Learning – The Easy Way

Linear algebra is the branch of mathematics concerning linear equations. It is the fundamental to geometry, for defining objects such as lines, planes, rotations. In this course we will discuss the Concepts of linear algebra needed for ML.

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Learn through our Scientifically Designed & Proven 5 Step Practice Oriented Learning Process
Linear Algebra for Machine Learning – The Easy Way
Includes
• 22 hands-on practice exercises
• 505 knowledge based questions
• Course Completion Certificate

Linear algebra is a sub-field of mathematics concerned with vectors, matrices, and linear transforms. It is a key foundation to the field of machine learning, from notations used to describe the operation of algorithms to the implementation of algorithms in code.

This course helps you to understand the vectors in different dimensions and their linear transformation to some other domain, matrix theory and various properties of matrices and its transformations, Eigenvalues and Eigenvectors and their role in dimensionality reduction and the applications of linear algebra for AI and ML.

Course Objectives

Upon successful completion of the course, the learner will be able to

• Master the various concepts in Linear Algebra.

• Learn how linear algebra plays an important role in reducing the complexity of the data processing in AI and ML algorithms.

Course Content
##### Eigen Values and Eigen Vectors

In this topic, you will learn about how to derive Eigenvalues and Eigenvectors from the matrices and how they are used to reduce dimension.

• 5 Hrs
• 127 Problems
##### Inner-Product Spaces, Linear Algebra Applications for ML & AI

In this topic, you will learn about how to find the inner product of the matrix and its properties and short cuts to find it. You will also learn how Linear Algebra is used in the field of AI and ML.

• 3 Hrs
• 66 Problems
##### Vector Spaces and Linear Transformations

In this topic, you will learn about the multidimensional vector space and its various representations. You will also understand the transformations between different domains.

• 3 Hrs 30 Mins
• 74 Problems
##### Subspaces, Span and Bases

In this topic, you will learn about Subspaces, span and Basis of a vector and how it reduces the complexity of representing the vectors.

• 3 Hrs
• 92 Problems
##### Matrix Theory

In this topic, you will learn about Grouping multiple vectors to form matrices and various types of matrices and its properties.

• 7 Hrs
• 168 Problems
You will Learn through our Scientifically Designed & Proven 5 Step Practice Oriented Learning Process
iLearn
In this session, you will find video lectures and other resources to learn the concepts.
iDesign
In this session, you will start designing and creating your own programs.
iExplore
In this session, you will find interesting set of activities which will make you explore more on the specific topic.
iAssess
In this session, you will have activities to self assess your knowledge and skills on the specific topic.
iAnalyse
In this session, you will find varieties of exercises to improve your code analysis, testing and debugging skills.
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