Math 01: Calculus for Data Science & Machine Learning

Math 0-1: Calculus for Data Science & Machine Learning
Last updated 1/2024
Duration: 13h50m | .MP4 1280×720, 30 fps(r) | AAC, 44100 Hz, 2ch | 4.18 GB

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
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
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

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