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ML Algorithms: Machine Learning Implementation Using Calculus & Probability

ML Algorithms: Machine Learning Implementation Using Calculus & Probability


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore the roles of probability, variance, and random vectors in implementing machine learning (ML) processes and algorithms. Discover how to apply calculus and differentiation using R and Python libraries.



Expected Duration (hours)
0.5

Lesson Objectives

ML Algorithms: Machine Learning Implementation Using Calculus & Probability

  • 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
  • Course Number:
    it_mlmdsndj_02_enus

    Expertise Level
    Intermediate