인프런 - 라이프타임 커리어 플랫폼

Limits of functions, derivatives, differentiation rules, derivative formulas, applications of differentiation

The core of data science is expertly mimicking humans' ability to systematically analyze vast data! Through this process, you can refresh fundamental mathematical knowledge needed to understand scientific modeling, which forms the basis of data science.

This course will help you build your knowledge of the fundamentals of integration of multivariable functions.

Through this course, you will learn the basics of optimization theory for multivariable functions and gain a comprehensive understanding of vector functions.

This course will provide students with a solid foundation in the fundamentals of differentiation in vector calculus (Calculus 2).

If calculus is too difficult? From the basics, step by step! Covers university-level calculus.

This course covers calculus at a first-year university level. In particular, it focuses on the integration of single-variable functions, allowing you to master various integration methods and technical approaches. As applications of integration, we will cover problems such as volume and surface area. You will also learn numerical integration methods using Python coding.

This course covers university-level first-year calculus. We focus intensively on single-variable function differentiation and learn various differentiation methods and formulas. As practical applications of differentiation, we select and study cases such as linear approximation, function increase and decrease, and optimization problems.

Theory, numerical approximations, intuition, and visualization in integral calculus. Enhance your math-coding skills.

Develop a deep understanding and intuition for calculus. Solve problems and implement algorithms by hand and in Python.

This course will help you build your knowledge of advanced integration of multivariable functions.

Were you a math-phobe during your school days? It's said that math is important in AI, but do you feel lost on how to approach it? The reason math is important in AI is because math is used in the computational processes of AI (Machine Learning and Deep Learning). For easy explanations of differentiation, linear algebra, and probability and statistics to understand AI, it is structured with simple explanations using graphs (coordinates) rather than mathematical derivations.