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Course Description

This course begins by helping you reframe real-world problems in terms of supervised machine learning. Through understanding the “ingredients” of a machine learning problem, you will investigate how to implement, evaluate, and improve machine learning algorithms. Ultimately, you will implement the k-Nearest Neighbors (k-NN) algorithm to build a face recognition system. Tools like the NumPy Python library are introduced to assist in simplifying and improving Python code.

Faculty Author

Kilian Weinberger

Benefits to the Learner

  • Define and reframe problems using machine learning (supervised learning) concepts and terminology
  • Identify the applicability, assumptions, and limitations of the k-NN algorithm
  • Simplify and make Python code efficient with matrix operations using NumPy, a library for the Python programming language
  • Build a face recognition system using the k-nearest neighbors algorithm
  • Compute the accuracy of an algorithm by implementing loss functions

Target Audience

  • Programmers
  • Developers
  • Data analysts
  • Statisticians
  • Data scientists
  • Software engineers
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Cornell Bowers Computing and Information Science
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