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Keep shoppingHands-On Calculus with Python for Data Science & ML
42 students
Hands-On Calculus with Python for Data Science & ML
Build a solid calculus foundation with Python to power your machine learning journey
Created byDr. Ron Erez
Last updated 12/2025
English
What you'll learn
- Understand and apply fundamental calculus concepts (limits, derivatives, multivariable gradients, and integrals)
- Use Python libraries such as NumPy, SymPy, Matplotlib, and SciPy to perform symbolic and numerical calculus computations and visualize mathematical concepts
- Connect calculus to real-world applications by analyzing cost functions, optimization problems, probability distributions, and data-driven models
- Develop problem-solving skills by combining calculus theory with Python coding to explore and implement techniques such as gradient descent, curve fitting, and
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Course content
9 sections • 77 lectures • 7h 25m total length
Requirements
- Basic Python knowledge (variables, functions, loops, lists/arrays). You don’t need to be an expert programmer.
- High school-level math (algebra, functions, basic graphing). No prior calculus experience is required — we’ll build it step by step.
- Computer with internet access and ability to run Jupyter Notebook/Google Colab (free, no installation needed).
- Curiosity and motivation to connect math with real-world data science and machine learning.
Description
Unlock the power of calculus with Python, the essential math skill for data science, machine learning, and real-world problem solving. This comprehensive course is designed not only to teach you calculus concepts but to help you apply them directly through Python programming — no dry theory, only practical, hands-on learning.
Whether you’re a student, developer, or aspiring data scientist, this course will guide you step-by-step from the fundamentals of functions and limits through derivatives, integrals, and multivariable calculus — all reinforced by coding exercises and real-world applications.
What You’ll Learn:
Core Calculus Concepts: Functions, limits, continuity, derivatives, integrals, optimization, and the fundamentals of multivariable calculus explained clearly with interactive Python examples.
Python for Math: Master libraries like SymPy for symbolic math, NumPy for numerical calculations, and Matplotlib for plotting calculus concepts visually.
Applied Problem Solving: Use calculus to solve real problems — from rate of change in physical systems to area under curves and optimization challenges.
Foundations for Machine Learning: Understand how calculus underlies machine learning algorithms — gradients, cost functions, and optimization techniques — giving you a head start on ML development.
Project-Based Learning: Build mini projects such as a derivative calculator, integral solver, and a simple gradient descent optimizer to solidify your understanding.
Bonus: Deploying a Shiny App: Learn how to create and deploy an interactive web app using Shiny to showcase your calculus projects and Python computations, making your work accessible and impressive for presentations, portfolios, or teaching.
Why This Course?
Unlike traditional calculus courses that overwhelm you with theory, or programming courses that ignore math foundations, this course bridges the gap. You’ll gain a deep, intuitive understanding of calculus, combined with practical Python skills that you can immediately apply in data science, engineering, or ML projects.
Who Should Enroll?
Students seeking a fresh, programming-focused approach to calculus.
Programmers and developers wanting to strengthen their math skills for machine learning or data science.
Anyone interested in learning how math and coding intersect to solve real-world problems.
Who this course is for:
- Aspiring data scientists and machine learning beginners who want to strengthen their math foundations with practical Python coding.
- Students in computer science, data analytics, or related fields who need a clear and applied introduction to calculus concepts.
- Self-taught programmers and professionals looking to fill gaps in their mathematical background to better understand machine learning algorithms.
- Anyone curious about the math behind AI who prefers learning through hands-on coding and visualization instead of abstract theory alone.