ML Algorithms: Machine Learning Implementation Using Calculus & Probability

Machine Learning    |    Intermediate
  • 10 videos | 30m 55s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 13 users Rating 4.4 of 13 users (13)
This course explores the use of multivariate calculus, derivative function representations, differentiation, and linear algebra to optimize ML (machine learning) algorithms. In 10 videos, learners will observe how to use probability theory to enable prediction and other analytical types in ML, including the role of probability in chain rule and Bayes' rule. First, you will explore the concepts of variance, covariance, and random vectors, before examining Likelihood and Posteriori estimation. Next, learn how to use estimation parameters to determine the best value of model parameters through data assimilation, and how it can be applied to ML. You will explore the role of calculus in deep learning, and the importance of derivatives in deep learning. Continue by learning optimization functions such as gradient descent, and whether to increase or decrease weight to maximize or minimize some metrics. You will learn to implement differentiation and integration in R and how to implement calculus derivatives, integrals using Python. Finally, you will examine the use of limits and series expansion in Python.

WHAT YOU WILL LEARN

  • Recognize the importance of probability in machine learning
    Identify the role of probability in the chain and bayes rules
    Define the concepts of variance, covariance and random vectors
    List the various estimation parameters that can be applied in machine learning, such as likelihood and posteriori estimation
    Identify the role of calculus when applied in deep learning
  • Demonstrate the implementation of differentiation and integration in r
    Implement calculus, derivatives, and integrals using python
    Demonstrate the use of limits and series expansions in python
    Declare symbols using python, find multiple derivatives using the diff function of sympy, and compute indefinite integrals using the sympy library

IN THIS COURSE

  • 1m 45s
  • 3m 12s
    After completing this video, you will be able to recognize the importance of probability in machine learning. FREE ACCESS
  • Locked
    3.  Chain and Bayes Rules
    2m 36s
    Learn how to identify the role of probability in the chain and Bayes rules. FREE ACCESS
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    4.  Variance and Random Vectors
    3m 30s
    Learn how to define the concepts of variance, covariance, and random vectors. FREE ACCESS
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    5.  Estimation Parameters
    3m 27s
    After completing this video, you will be able to list the various estimation parameters that can be applied in machine learning, such as Maximum Likelihood and Bayesian estimation. FREE ACCESS
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    6.  Deep Learning and Calculus
    2m 56s
    In this video, you will learn how to identify the role of calculus in deep learning. FREE ACCESS
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    7.  R and Calculus
    4m 9s
    Learn how to apply differentiation and integration in R. FREE ACCESS
  • Locked
    8.  Calculus in Python
    3m 13s
    In this video, you will learn how to implement calculus, derivatives, and integrals using Python. FREE ACCESS
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    9.  Series Expansion in Python
    3m 10s
    In this video, you will learn how to use limits and series expansions in Python. FREE ACCESS
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    10.  Exercise: Derivatives and Integrals with SymPy
    2m 58s
    In this video, you will learn how to declare symbols using Python, find multiple derivatives using the diff function of SymPy, and compute indefinite integrals using the SymPy library. FREE ACCESS

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