You can not escape mathematics if you wish to understand how machine learning algorithms work. Linear algebra, statistics, probability, and calculus are the four key sub-fields that are pre-requisite to learning the internals of the algorithms. The post lists down the courses to learn calculus, but let's first understand the need to learn calculus. 

Why Do You Need to Learn Calculus?

You need to know calculus to calculate derivatives, for example, to adjust the neuron weights in the backpropagation of a neural network. Essentially, you need calculus to comprehend the association between a set of inputs and output variables. 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.

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Pre-requisites to Learn 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.

You should have a reasonable understanding of algebra, geometry, and trigonometry to grasp calculus. Further, I would highly recommend reading this excellent article by Khan Academy that emphasizes the key skills before starting a course in calculus. 

List of Five Free Courses to Learn Calculus

1. Calculus 1 by Khan Academy

Khan Academy videos and explanations make learning any new mathematics concept very easy, even for a newbie, and are highly recommended in general. The calculus course covers concepts like limits, continuity, integrals, derivatives - basics and advanced topics like chain rule, second derivatives, etc.

2. Calculus for Machine Learning by Jon Krohn

It is a playlist of 56 videos by Jon Krohn. It covers the foundations of calculus with topics like partial derivatives, delta method, power rule, etc. Jon has also created a similar course on linear algebra as part of foundational concepts to understand contemporary machine learning and data science techniques.

3. Mathematics for Machine Learning: Multivariate Calculus– Imperial College London

 
This course is a part of “Mathematics for Machine Learning Specialization” hosted at Coursera. It is a self-paced course with flexible deadlines making it suitable for working professionals alike. It takes a total of 18 hours to complete the course and is offered by the Imperial College of London. 

As per the course listing page, it helps the learners build an understanding of the following concepts:

• Multivariate calculus to build many common machine learning techniques 
• The gradient of a function
• Build approximations to functions
• Role of neural networks in training neural networks
• Application of calculus in linear regression models

You can also watch the video playlist of this online specialization here.

4. Introduction to Calculus by the University of Sydney

It is an intermediate-level course that takes 59 hours to complete over five weeks. The course is taught by Associate Prof David Easdown and is offered by the University of Sydney. The course explains the foundational concepts like precalculus, tangents, limits, etc. The course is hosted at Coursera and provides a shareable certificate upon completion. 

5. 18.01x Single Variable Calculus by MIT

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 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  

Quoting from the course webpage, learners will understand 

• Various ways to interpret the derivative and integral of a function and how to compute these quantities
• How to use linear and quadratic approximations of functions to simplify computations and gain insight into the system behavior
• Applications of the derivative and integral such as in optimizing the cost of production or computing the stress on a construction beam
• The calculus of parameterized curves and polar coordinates
• How to approximate complicated functions by a series of simpler functions

The program is a series of three courses - Differentiation, Integration and Coordinate Systems, and Infinite Series. Each course requires a time commitment of 6-10 hours per week and spans around 13-15 weeks.

Bonus

One bonus tip to learn calculus in seven days is a mini-course from Jason Brownlee. He is an AI/ML expert with a knack to explain theoretical and maths-heavy machine learning concepts in the form of practical and code-oriented formats. 

His mini-course covers common calculus concepts used in machine learning along with python exercises. The course spans seven days and expects the learner to have a foundational knowledge of machine learning models, python, and linear algebra. The key concepts covered in this course include differentiation, integration, the gradient of a vector function, backpropagation, optimization, etc.

At the end of each lesson, the course suggests you complete an assignment similar to what is taught in the lesson previously and shares the results towards the end too.   

Summary

In an endeavor to enable you to become a data scientist who is well-versed in the internals of machine learning algorithms, the post shares five free courses to master calculus concepts. Hope you find the list of courses shared in this post useful in your data science journey. 

 
 
Vidhi Chugh is an award-winning AI/ML innovation leader and an AI Ethicist. She works at the intersection of data science, product, and research to deliver business value and insights. She is an advocate for data-centric science and a leading expert in data governance with a vision to build trustworthy AI solutions.