Math 0-1: Calculus for Data Science & Machine Learning
Год выпуска: 2/2025
Производитель: Udemy, Lazy Programmer Inc.
Сайт производителя:
https://www.udemy.com/course/calculus-data-science/?
Автор: Lazy Programmer Inc.
Продолжительность: 14h 28m 14s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Английский
Описание:
What you'll learn
- Limits, limit definition of derivative, derivatives from first principles
- Derivative rules (chain rule, product rule, quotient rule, implicit differentiation)
- Integration, area under curve, fundamental theorem of calculus
- Vector calculus, partial derivatives, gradient, Jacobian, Hessian, steepest ascent
- Optimize (maximize or minimize) a function
- l'Hopital's Rule
- Newton's Method
Requirements
- Firm understanding of high school math
Description
Common scenario: You try to get into
machine learning and
data science, but there's SO MUCH MATH.
Either you never studied this math, or you studied it so long ago you've forgotten it all.
What do you do?
Well my friends, that is why I created this course.
Calculus is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.
If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.
Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.
Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.
This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the
Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.
Are you ready?
Let's go!
Suggested prerequisites:
- Firm understanding of high school math (functions, algebra, trigonometry)
Who this course is for:
- Anyone who wants to learn calculus quickly
- Students and professionals interested in machine learning and data science but who've gotten stuck on the math
What you'll learn
- Limits, limit definition of derivative, derivatives from first principles
- Derivative rules (chain rule, product rule, quotient rule, implicit differentiation)
- Integration, area under curve, fundamental theorem of calculus
- Vector calculus, partial derivatives, gradient, Jacobian, Hessian, steepest ascent
- Optimize (maximize or minimize) a function
- l'Hopital's Rule
- Newton's Method
Additional courses by Lazy Programmer:
[Udemy, Lazy Programmer Inc.] Math 0-1: Matrix Calculus in Data Science & Machine Learning [12/2024, ENG]
[Udemy, Lazy Programmer Inc.] Math 0-1: Linear Algebra for Data Science & Machine Learning [8/2023, ENG]
[Udemy, Lazy Programmer Inc.] Math 0-1: Probability for Data Science & Machine Learning [2/2025, ENG]
Формат видео: MP4
Видео: avc, 1280x720, 16:9, 30000 к/с, 394 кб/с
Аудио: aac, 44.1 кгц, 128 кб/с, 2 аудио
Изменения/Changes
Version 2023/8 compared to 2023/2 has increased the number of 14 lessons and the duration of 2 hours and 17 minutes. English subtitles have also been added to the course.
Version 2025/2 compared to 2023/8 has increased the number of 4 lessons and the duration of 31 minutes.
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