# ML Algorithms: Machine Learning Implementation Using Calculus & Probability

Machine Learning
• 10 Videos | 34m 55s
• Includes Assessment
Likes 19
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

• 1.
Course Overview
• 2.
Probability and Machine Learning
• 3.
Chain and Bayes Rules
• 4.
Variance and Random Vectors
• 5.
Estimation Parameters
• 6.
Deep Learning and Calculus
• 7.
R and Calculus
• 8.
Calculus in Python
• 9.
Series Expansion in Python
• 10.
Exercise: Derivatives and Integrals with SymPy

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