Math 0-1: Calculus for Data Science & Machine Learning
Math 0-1: Calculus for Data Science & Machine Learning Course
A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers
This Math 0-1: Calculus for Data Science & Machine Learning Course is designed to help you learn calculus quickly and efficiently. It covers Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). The course is taught by Lazy Programmer Inc., an experienced online educator with over 10 years of experience in data science and machine learning.
The Math 0-1: Calculus for Data Science & Machine Learning Course is aimed at students and professionals interested in machine learning and data science but who’ve gotten stuck on the math. It’s also suitable for anyone who wants to learn calculus quickly. The course is structured in a way that provides you with skills directly applicable to machine learning and data science, so you can start applying them today.
The course includes machine learning-focused material you wouldn’t normally see in a regular college course. It even demonstrates many of the concepts using the Python programming language. The course is fully optimized for SEO and is designed to help you learn calculus quickly and efficiently.
What you’ll learn in Math 0-1: Calculus for Data Science & Machine Learning Course
- 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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