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Applied Machine Learning and Lifecycle

Description

You will cover the lifecycle of machine learning working with Python and R, including processing unstructured data, feature engineering, dimensionality reduction, model selection and optimization, performance evaluation, and model improvement. You will cover advanced algorithms for complex problems that require specialized methods. You will gain hands-on experience with advanced algorithms such as ensemble methods, sequence models, association rule mining, and neural networks. You will construct models using data from a variety of application domains. Time will also be allotted throughout the course to expose students to various ethical and public policy considerations of their work. * successful completion of ML1000 is required to begin ML1010.    

Note: Check with the institution regarding start/end dates, prices, and delivery method. These may vary according to program, section, and/or semester.
  

Overview

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  • Institution: York University
  • Level: University
  • Language: English
  • Course Code: CSML1010
  • Delivery Method: Blended/Hybrid Learning

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Disclaimer:
Check with the institution regarding start/end dates, prices, and delivery method. These may vary according to program, section, and/or semester.