# 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