Math 0-1: Calculus for Data Science & Machine Learning
A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers Math 0-1: Calculus for Data Science & Machine Learning
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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. Math 0-1: Calculus for Data Science & Machine Learning
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 "Math 0-1: Calculus for Data Science & Machine Learning" 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.
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
