Math 0-1: Calculus for Data Science & Machine Learning
Math 0-1: Calculus for Data Science & Machine Learning
A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers
Created by Lazy Programmer Inc., Lazy Programmer Team | 13.5 hours on-demand video course
This Math 0-1: Calculus for Data Science & Machine Learning 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 Math 0-1: Calculus for Data Science & Machine Learning 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
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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
