₹0 for students · Certificate · Beginner

Calculus for Computing

4 mini courses, 12 lessons: growth, derivatives and gradients, integrals, and numerical approximation

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12 lessons · ~2 hours · 24-question final exam · pass mark 70%

About the course

What you’ll learn

The calculus that programmers and ML engineers actually use. Understand growth rates and logarithms, derivatives as rates of change, the chain rule behind backpropagation, gradients and gradient descent, integrals as accumulation, and the approximations computers rely on, from Taylor series to Newton's method and floating-point error. Every idea is checked numerically in Python. Pass every lesson quiz and the final exam to earn a verifiable certificate.

CalculusDerivativesGradient descentNumerical methodsMaths for ML

Part of the career tracks: Machine Learning Engineer, Computer Science Foundations

How the certificate works

  1. Read each lesson’s study guide, then the full lesson and any notebooks or exercises.
  2. Pass every lesson quiz with 70% or more.
  3. Pass the final exam: 24 questions in 36 minutes.
  4. Get a certificate with a public verification link, and add it to LinkedIn.

Syllabus

12 lessons in 4 mini courses

Each mini course takes about an hour. Already know one? Test out in the app and skip it.

1. Functions and change

Compare growth rates and explain limits and derivatives.

1
Functions, Exponentials and Logarithms
10 min · Linear, polynomial and exponential functions grow at very different rates.
2
Limits and Continuity
10 min · A limit is the value a function approaches near a point.
3
The Derivative
12 min · The derivative is the instantaneous rate of change: the slope.

2. Derivatives at work

Use the chain rule, find optima and run gradient descent.

4
Derivative Rules and the Chain Rule
12 min · Sum, constant multiple and product rules.
5
Finding Maximums and Minimums
10 min · At a smooth max or min, the derivative is zero.
6
Partial Derivatives and Gradients
10 min · A partial derivative changes one input and holds the others fixed.
7
Gradient Descent
12 min · Gradient descent: w ← w − learning rate × gradient.

3. Integrals

Interpret integrals as accumulation and compute them numerically.

8
Integrals as Accumulation
10 min · Integrals add up rates: speed → distance.
9
Numerical Integration and Simulation
10 min · Approximate areas with thin strips.

4. Approximation

Approximate functions, find roots and reason about floating-point error.

10
Taylor Series and Approximations
10 min · Taylor series: a polynomial from derivatives at one point.
11
Newton's Method and Root Finding
10 min · Root finding solves f(x) = 0: rates, square roots, optima.
12
Floating-Point Numbers and Error
10 min · Floating point: sign, fraction, exponent; doubles give ~15–16 digits.

Written by Vikosha. Lessons, questions and exercises are original. Licence: Original Vikosha course..