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# Informit – Calculus for Machine Learning LiveLessons

0 Informit – Calculus for Machine Learning LiveLessons (Video Training) Free Download. introduces the mathematical field of calculus — the study of rates of change — from the ground up.

## Informit – Calculus for Machine Learning LiveLessons (Video Training) Description

Calculus for Machine Learning LiveLessons introduces the mathematical field of calculus — the study of rates of change — from the ground up.

You’ll also learn how to apply automatic differentiation within the popular TensorFlow 2 and PyTorch machine learning libraries. Later lessons build on single-variable derivative calculus to detail gradients of learning (which are facilitated by partial-derivative calculus) and integral calculus (which determines the area under a curve and comes in handy for myriad tasks associated with machine learning).

Skill Level

• Intermediate

### Course Requirements

• Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow. If you are comfortable dealing with quantitative information, such as understanding charts and rearranging simple equations, you should be well prepared to follow along with all the mathematics.
• Programming: All code demos are in Python, so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.

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### What’ll you learn

• Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning.
• Compute the derivatives of functions, including by using AutoDiff in the popular TensorFlow 2 and PyTorch libraries.
• Be able to grasp the details of the partial-derivative, multivariate calculus that is common in machine learning papers and in many other subjects that underlie ML, including information theory and optimization algorithms.
• Use integral calculus to determine the area under any given curve, a recurring task in ML applied, for example, to evaluate model performance by calculating the ROC AUC metric.

### Who Should Take This Course

• People who use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms and would like to understand the fundamentals underlying the abstractions, enabling them to expand their capabilities
• Software developers who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
• Data scientists who would like to reinforce their understanding of the subjects at the core of their professional discipline
• Data analysts or AI enthusiasts who would like to become data scientists or data/ML engineers, and so are keen to deeply understand the field they’re entering from the ground up (a very wise choice!)

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