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    Python Calculus For Data Science And Machine Learning

    Posted By: ELK1nG
    Python Calculus For Data Science And Machine Learning

    Python Calculus For Data Science And Machine Learning
    Published 8/2025
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 1003.32 MB | Duration: 3h 27m

    Build a solid calculus foundation with Python to power your machine learning journey

    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

    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.

    Overview

    Section 1: Introduction

    Lecture 1 Introduction

    Section 2: Python Refresher

    Lecture 2 Introduction to the Python Refresher

    Lecture 3 Variables

    Lecture 4 Basic Math

    Lecture 5 Lists

    Lecture 6 Tuples

    Lecture 7 Dictionaries

    Lecture 8 Conditional Statements

    Lecture 9 Recap Data Types

    Section 3: Introducing Sympy

    Lecture 10 Introducing Sympy

    Lecture 11 Symbolic Functions vs Expressions

    Lecture 12 Defining Concrete Functions with Lambda

    Lecture 13 Substitution and Evaluation

    Lecture 14 Rational Numbers in Sympy

    Lecture 15 Plotting a Function from R -> R

    Lecture 16 Plotting a Function from R x R -> R

    Lecture 17 Simplify, Factor and Expand

    Lecture 18 Advanced Topics: Equality, Better Pretty Print, Rationalize, Collect, Cancel

    Lecture 19 Systems of Equations

    Section 4: Exploring Functions in Sympy

    Lecture 20 Introduction

    Lecture 21 THEORY - What is a Function?

    Lecture 22 THEORY - Linear Functions and Linear Equations

    Lecture 23 THEORY - Quadratic Functions and Quadratic Equations

    Lecture 24 Functions in Python

    Lecture 25 Introducing the Quadratic Equation

    Lecture 26 Graphing the Quadratic Equation, Discriminant, Vertex and Roots

    Lecture 27 Linear Functions and Linear Equations

    Lecture 28 Exponential Functions - Overview

    Lecture 29 Exponential Functions in Sympy

    Lecture 30 Logarithmic Functions

    Section 5: Limits

    Lecture 31 THEORY - What is a limit?

    Lecture 32 Calculating a Limit at a Removable Discontinuity

    Lecture 33 SOLUTION f(x) = (9 - x) : (3 - sqrt(x)) where x tends to 9

    Lecture 34 Example where x tends to Infinity

    Lecture 35 Left and Right Limits and Vertical Asymptotes

    Lecture 36 (e^x - 1 - x) / x^2 where x -> 0

    Lecture 37 Graphing the Numerator and Denominator of (e^x - 1 - x) / x^2

    Lecture 38 Euler's Limit

    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.