#1005FreeIntermediate

Calculus Made Easy: Limits, Derivatives & Integrals

A visual and intuitive approach to calculus. Understand limits, derivatives, and integrals through animations, real-world examples, and step-by-step problem solving.

ID: 1005
4.8
(445)
3,800 Students Enrolled
3 Hours
4 Lessons
Arabic

Instructor: Dr. Fatima Al-Zahra

What You Will Learn

Understand the intuition behind limits and continuity
Apply differentiation rules to solve real-world optimization problems
Compute definite and indefinite integrals
Use calculus in physics, engineering, and economics applications

Who This Course Is For

High school students preparing for college-level mathematics
University freshmen taking their first calculus course
Engineering and physics students who need a calculus refresher
Self-learners who want to understand calculus intuitively

Prerequisites

Solid understanding of algebra and basic trigonometry
Familiarity with functions, graphs, and coordinate geometry
A scientific calculator (or a free online calculator)
No prior calculus knowledge required

Course Content

Derivatives Practice ProblemsPreview Course
40m

This Course Includes

3 Hours
4 Lessons
Arabic
Completion Certificate

Instructor

Dr. Fatima Al-Zahra

Dr. Fatima Al-Zahra

PhD in Applied Mathematics from MIT. Makes complex mathematical concepts accessible and engaging for all learners.

Student Reviews

4.7
3 Reviews
5
2
4
1
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Tariq Nasser

Tariq Nasser

August 10, 2025

Dr. Fatima has a gift for making calculus intuitive. The animations for limits were brilliant. As a math teacher myself, I learned new ways to explain these concepts to my students.

Instructor

That means a lot coming from a fellow educator, Tariq! Thank you.

Karim Bouazizi

Karim Bouazizi

September 20, 2025

Finally someone who explains the chain rule in a way that makes sense! The practice problems are well-graded in difficulty. Amazing that this is free.

Hassan Mahmoud

Hassan Mahmoud

October 5, 2025

Essential for anyone going into data science. The derivatives section gave me the mathematical intuition I needed for understanding gradient descent in ML.