ML Algorithms: Multivariate Calculation & Algorithms

Machine Learning    |    Intermediate
  • 10 videos | 38m 56s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 25 users Rating 4.5 of 25 users (25)
Learners can explore the role of multivariate calculus in machine learning (ML), and how to apply math to data science, ML, and deep learning, in this 10-video course examining several ML algorithms, and showing how to identify different types of variables. First, learners will observe how to implement multivariate calculus, derive function representations of calculus, and utilize differentiation and linear algebra to optimize ML algorithms. Next, you will examine how to use advanced calculus and discrete optimization, to implement robust, and high-performance ML applications. Then you will learn to use R and Python to implement multivariate calculus for ML and data science. You will learn about partial differentiation, and its application on vector calculus and differential geometry, and the use of product rule and chain rule. You will examine the role of linear algebra in ML, and learn to classify the techniques of optimization by using gradient and Jacobian matrix. Finally, you will explore Taylor's theorem and the conditions for local minimum.

WHAT YOU WILL LEARN

  • Recognize the role of multivariate calculus in machine learning
    Describe functions in calculus
    Define the concepts of gradient and derivative and describe their applications on the functions of variables
    List the capabilities of the product and chain rules
    Define partial differentiation and its application in vector calculus and differential geometry
  • Recognize the importance of linear algebra in machine learning
    Describe optimization techniques when using gradient and jacobian matrix
    Define taylor's theorem and specify the conditions for local minima
    List various multivariate operations that can be used in multivariate calculus, compare the differences between a gradient and derivative, recall examples of partial differential equation, and specify the domains where linear algebra is implemented

IN THIS COURSE

  • 1m 51s
  • 3m 48s
    Upon completion of this video, you will be able to recognize the role of multivariate calculus in machine learning. FREE ACCESS
  • Locked
    3.  Function Representation
    3m 9s
    After completing this video, you will be able to describe functions in calculus. FREE ACCESS
  • Locked
    4.  Gradient and Derivative
    4m 7s
    In this video, you will learn how to define the concepts of gradient and derivative and describe their applications on functions of variables. FREE ACCESS
  • Locked
    5.  Product and Chain Rule
    4m 13s
    Upon completion of this video, you will be able to list the capabilities of the product and chain rules. FREE ACCESS
  • Locked
    6.  Partial Differentiation
    4m 57s
    Learn how to define partial differentiation and its applications in vector calculus and differential geometry. FREE ACCESS
  • Locked
    7.  Linear Algebra
    6m 20s
    After completing this video, you will be able to recognize the importance of linear algebra in machine learning. FREE ACCESS
  • Locked
    8.  Gradient and Jacobian Matrix
    2m 42s
    After completing this video, you will be able to describe optimization techniques when using the Gradient and Jacobian matrix. FREE ACCESS
  • Locked
    9.  Taylor's Theorem and Local Minima
    6m 4s
    Find out how to define Taylor's theorem and specify the conditions for local minima. FREE ACCESS
  • Locked
    10.  Exercise: Multivariate Operations for Calculus
    1m 46s
    Upon completion of this video, you will be able to list various multivariate operations that can be used in multivariate calculus, compare the differences between a gradient and derivative, recall examples of partial differential equations, and specify the domains where linear algebra is implemented. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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