Machine Learning: Algorithms and Applications Overview
From robotics, speech recognition, and analytics, to finance and social network analysis, machine learning comprises one of the most useful scientific toolsets of our age. This course provides an overview of the core principles of machine learning using a hands-on, project-based curriculum. There is an intense focus on implementing popular machine learning algorithms to solve real problems using real data.
Who is this course for?
This is designed for people working in any number of data-intensive fields, including consulting, finance, IT, healthcare, and logistics, as well as for recent college graduates and entrepreneurs interested or specializing in those fields.
Firm knowledge of the Python programming environment.
Basic understanding of vector and matrix algebra (how to add and multiply vectors/matrices), as well as basic understanding of the notion of a mathematical function (e.g., understanding what f(x)=x^2 or f(x) = sin(x) means).
Basic calculus and linear algebra is helpful but not required (e.g., how to take derivatives, what a linear system of equations is, etc.). A quick refresher on these topics will be provided. (Note: Knowledge of statistics is not required for this course.)
Upon completion of the Machine Learning course, students have: