Applied Calculus for Data Science & ML with Python

Packt via Coursera

Go to class Write review

Details

Go to class

Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

9 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

Learn essential calculus concepts using Python, focusing on data science and machine learning applications. Explore limits, derivatives, and optimization techniques through hands-on coding projects, building practical skills to solve real-world data problems. 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. This course is ideal for anyone looking to strengthen their math skills with practical Python coding. It is particularly beneficial for aspiring data scientists and machine learning enthusiasts who want to build a solid foundation in calculus. Students in computer science, data analytics, or related fields will also find the course useful for applying calculus concepts to real-world data problems. No prior calculus experience is required, but a basic understanding of Python will be helpful. This course takes a hands-on approach to learning calculus through Python, starting with foundational Python skills and progressing to advanced calculus concepts like derivatives, integrals, and optimization. Each section builds on the previous one, ensuring learners gain practical experience with real-world applications in data science and machine learning. This course is based on Applied Calculus for Data Science and Machine Learning with Python, by Ron Erez. This video is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Introduction
- This module introduces the foundational aspects of the course, including key concepts and the structure of applied calculus using Python. Learners will gain an overview of the course roadmap and develop a clear understanding of how Python is utilized in calculus applications.
Python Refresher
- This module provides a comprehensive review of essential Python programming concepts, including variables, data types, and control structures, with a focus on their application in mathematical and data science contexts. Learners will reinforce foundational skills needed for advanced topics like calculus and data analysis.
Introducing Sympy
- This module introduces learners to the Sympy library for symbolic mathematics, covering key concepts like symbolic functions, expressions, substitution, plotting, and solving equations. Learners will develop skills in manipulating mathematical expressions and applying Sympy to real-world calculus problems. The module also explores advanced features such as simplification, factoring, and rationalizing expressions.
Exploring Functions in Sympy
- This module covers the fundamentals of functions in Sympy, including their mathematical definitions, applications in calculus, and implementation in Python. It explores linear, quadratic, exponential, and logarithmic functions, their graphical representations, and how to use them in data science problems. Learners will gain both theoretical understanding and practical coding skills.
Limits
- This module explores the fundamental concept of limits in calculus, covering how to calculate limits at different points, including removable discontinuities, infinity, and zero. It also delves into graphical interpretations and the significance of key limits like Euler's. Learners will gain the ability to analyze and solve limit problems in various contexts.
Derivatives
- This module explores the fundamentals of derivatives, including their definitions, properties, and applications in calculus. Learners will gain a deep understanding of how to calculate and interpret derivatives, as well as how to use computational tools like Sympy for symbolic differentiation. The module also covers key rules and techniques for applying derivatives in mathematical analysis and problem-solving.
Integration
- This module introduces the fundamental concepts of integration, including definite and indefinite integrals, anti-derivatives, and practical problem-solving techniques. Learners will develop a strong foundation in integral calculus and improve their ability to apply integration methods to real-world problems.
Higher Derivatives and Gradients
- This module explores the concepts of partial derivatives, gradients, and higher-order derivatives, including the Hessian matrix and Newton's method. Learners will gain practical skills in computing and visualizing these mathematical tools, with applications in optimization and machine learning.
Applications in Data Science and Machine Learning
- This module explores the application of mathematical concepts like derivatives and optimization techniques in data science and machine learning. Learners will gain insights into how cost functions are minimized, compare different optimization methods, and apply these principles to real-world problems. By the end, they will be able to analyze and implement optimization strategies effectively.