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.
CALCULUS 1 ONLINE FREE
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. You can not escape mathematics if you wish to understand how machine learning algorithms work.
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