How to approximate complicated functions by a series of simpler functions.The calculus of parameterized curves and polar coordinates.Applications of the derivative and integral such as in optimizing the cost of production or computing the stress on a construction beam.How to use linear and quadratic approximations of functions to simplify computations and gain insight into the system behavior.Various ways to interpret the derivative and integral of a function and how to compute these quantities.Quoting from the course webpage, learners will understand The key takeaway from the course is that it not only explains the geometrical interpretation but also aids the learning with real-world application of such mathematical concepts It is an exhaustive and advanced level program offered by MIT to master the concepts of calculus and learn how to compute derivatives and integrals. The course is hosted at Coursera and provides a shareable certificate upon completion.ĥ. The course explains the foundational concepts like precalculus, tangents, limits, etc. The course is taught by Associate Prof David Easdown and is offered by the University of Sydney. It is an intermediate-level course that takes 59 hours to complete over five weeks. Introduction to Calculus by the University of Sydney You can also watch the video playlist of this online specialization here.Ĥ. Application of calculus in linear regression models.Role of neural networks in training neural networks.Multivariate calculus to build many common machine learning techniques.It takes a total of 18 hours to complete the course and is offered by the Imperial College of London.Īs per the course listing page, it helps the learners build an understanding of the following concepts: It is a self-paced course with flexible deadlines making it suitable for working professionals alike. This course is a part of “Mathematics for Machine Learning Specialization” hosted at Coursera. Mathematics for Machine Learning: Multivariate Calculus– Imperial College London Jon has also created a similar course on linear algebra as part of foundational concepts to understand contemporary machine learning and data science techniques.ģ. It covers the foundations of calculus with topics like partial derivatives, delta method, power rule, etc. It is a playlist of 56 videos by Jon Krohn. Calculus for Machine Learning by Jon Krohn The calculus course covers concepts like limits, continuity, integrals, derivatives - basics and advanced topics like chain rule, second derivatives, etc.Ģ. Khan Academy videos and explanations make learning any new mathematics concept very easy, even for a newbie, and are highly recommended in general. List of Five Free Courses to Learn Calculus Further, I would highly recommend reading this excellent article by Khan Academy that emphasizes the key skills before starting a course in calculus. You should have a reasonable understanding of algebra, geometry, and trigonometry to grasp calculus. Now that we understand why calculus is an important prerequisite to understanding how machine learning algorithms work, let's learn what skills you need to learn calculus. Source: Image by storyset on Freepik Pre-requisites to Learn Calculus This study of multiple attributes is called multivariate calculus and is used in calculating the minimum and maximum values of a function, derivatives, cost functions, etc. Essentially, you need calculus to comprehend the association between a set of inputs and output variables. You need to know calculus to calculate derivatives, for example, to adjust the neuron weights in the backpropagation of a neural network. The post lists down the courses to learn calculus, but let's first understand the need to learn calculus. Linear algebra, statistics, probability, and calculus are the four key sub-fields that are pre-requisite to learning the internals of the algorithms. Also you can rearrange the terms or factors according to the commutative property so your answer may come in many equivalent forms, but it is necessary that the derivatives be multiplied by the other function.You can not escape mathematics if you wish to understand how machine learning algorithms work. Once you have carefully differentiated both factors combine the derivatives and the original functions according to the product rule: $f(x)\frac$.ĭo not simply differentiate each factor and multiply the derivatives. These derivatives could be complicated and require several steps, but they shoud be easier than differentiating the original function. Once you have identified the two factors, $f(x)$ and $g(x)$, find the derivative of each factor. Can you identify an $f(x)$ and $g(x)$ such that the function exactly equals $f(x)g(x)$? If yes, use the product rule. Use the product rule when the function consists of the product of two simpler functions.
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