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Book Overview & Buying
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Table Of Contents
Applied Calculus for Data Science and Machine Learning with Python
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Applied Calculus for Data Science and Machine Learning with Python
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Overview of this book
Unlock the power of calculus in data science and machine learning through Python in this comprehensive course. You will start by mastering fundamental concepts like limits, derivatives, and integrals, building a strong foundation in mathematical theory. The course progresses to cover more advanced topics like multivariable calculus and optimization techniques, ensuring you have the tools needed for real-world data analysis.
Throughout the course, you’ll work with Python libraries such as SymPy, NumPy, and Matplotlib to perform symbolic and numerical calculations. You will also apply these skills in practical scenarios like optimization problems and cost function analysis. By integrating both math and programming, this course prepares you for more advanced applications in data science and machine learning.
The course is designed to be project-driven. You’ll build mini-projects, including a derivative calculator and a gradient descent optimizer, to reinforce your understanding. These projects offer practical skills that can be applied directly to machine learning and data science tasks, making the course both comprehensive and hands-on.
Table of Contents (9 chapters)
Introduction
Python Refresher
Introducing Sympy
Exploring Functions in Sympy
Limits
Derivatives
Integration
Higher Derivatives and Gradients
Applications in Data Science and Machine Learning
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