Course details

Linear Algebra & Probability: Advanced Linear Algebra

Linear Algebra & Probability: Advanced Linear Algebra


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Learners will discover how to apply advanced linear algebra and its principles to derive machine learning implementations in this 14-video course. Explore PCA, tensors, decomposition, and singular-value decomposition, as well as how to reconstruct a rectangular matrix from singular-value decomposition. Key concepts covered here include how to use Python libraries to implement principal component analysis with matrix multiplication; sparse matrix and its operations; tensors in linear algebra and arithmetic operations that can be applied; and how to implement Hadamard product on tensors by using Python. Next, learn how to calculate singular-value decomposition and reconstruct a rectangular matrix; learn the characteristics of probability applicable in machine learning; and study probability in linear algebra and its role in machine learning. You will learn types of random variables and functions used to manage random numbers in probability; examine the concept and characteristics of central limit theorem and means and learn common usage scenarios; and examine the concept of parameter estimation and Gaussian distribution. Finally, learn the characteristics of binomial distribution with real-time examples.



Expected Duration (hours)
1.7

Lesson Objectives

Linear Algebra & Probability: Advanced Linear Algebra

  • discover the key concepts covered in this course
  • use Python libraries to implement principal component analysis with matrix multiplication
  • describe sparse matrix and the operations that can be performed on sparse matrix
  • define the concept of tensors in linear algebra and list the arithmetic operations that can be applied on tensors
  • implement Hadamard product on tensors using Python
  • describe singular-value decomposition and how to calculate it
  • reconstruct a rectangular matrix from single-value decomposition
  • recognize the characteristics of probability that are applicable in machine learning
  • describe probability in linear algebra and its role in machine learning
  • recall the types of random variables and the functions that can be used to manage random numbers in probability
  • describe the concept and characteristics of central limit theorem and means and recognize common usage scenarios
  • describe parameter estimation and distribution using Gaussian
  • describe binomial distribution and its characteristics
  • recall the arithmetic operations that can be applied on tensors, list the features of multivariate statistics that are applicable in machine learning, and implement Hadamard product on tensors using Python
  • Course Number:
    it_mllapbdj_02_enus

    Expertise Level
    Intermediate