Linear Algebra & Probability: Advanced Linear Algebra

Machine Learning
  • 14 Videos | 1h 48m 55s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 52s
    UP NEXT
  • Playable
    2. 
    Matrix and PCA
    6m 22s
  • Locked
    3. 
    Sparse Matrix
    9m 33s
    FREE ACCESS
  • Locked
    4. 
    Tensor Arithmetic
    5m 26s
    FREE ACCESS
  • Locked
    5. 
    Hadamard Product and Tensors
    3m 55s
    FREE ACCESS
  • Locked
    6. 
    Singular-Value Decomposition
    5m 51s
    FREE ACCESS
  • Locked
    7. 
    Reconstruct Rectangular Matrix Using SVD
    6m 48s
    FREE ACCESS
  • Locked
    8. 
    Probability
    15m 2s
    FREE ACCESS
  • Locked
    9. 
    Probability Basics and Propositions
    11m 47s
    FREE ACCESS
  • Locked
    10. 
    Random Variable
    10m 3s
    FREE ACCESS
  • Locked
    11. 
    Central Limit Theorem
    7m 36s
    FREE ACCESS
  • Locked
    12. 
    Parameter Estimation and Gaussian Distribution
    6m 39s
    FREE ACCESS
  • Locked
    13. 
    Binomial Distribution
    7m 17s
    FREE ACCESS
  • Locked
    14. 
    Exercise: Tensor Arithmetic and Hadamard Product
    4m 44s
    FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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