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Course DescriptionThis 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 AuthorKilian 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
- Data analysts
- Data scientists
- Software engineers
Applies Towards the Following Certificates
- Machine Learning : Required Courses