Course description

This course covers fundamental concepts and components of machine learning (ML) such as regression, classification and clustering, and essential tools, such as modern data visualization, and skills to fully understand the field of ML. This course is designed as an elective for the Professional Development Certificate in Business Intelligence and Data Analytics and a stand-alone course for those with data science and coding backgrounds. 

Additionally, the course is packed with practical exercises based on real-life examples, so you will also get some hands-on practice building your own models. This course will then ladder to an AI micro-credential that is currently under development. 

Learning objectives

Upon completion of this course, you will be able to:

  • explain data pre-processing steps, data exploring and visualization
  • differentiate between supervised and unsupervised machine learning techniques
  • practice optimization techniques (e.g. SGD)
  • form linear models and extensions to nonlinearity using kernel methods
  • understand model complexity, overfitting and model regularization
  • recognize nonparametric models such as K-Nearest Neighbors (KNN)
  • describe collaborative methods: boosting, bagging and random forests
  • understand artificial neural networks with Sklearn, TensorFlow and Keras
  • build deep models using the Keras Sequential and Functional API
  • apply pattern recognition and regression models with deep learning
  • decipher metrics and output evaluations for regression problems
  • interpret metrics (e.g. confusion matrix, AUC, etc.) and output evaluation/interpretation for classifications
  • understand unsupervised methods: dimensionality reduction, autoencoders and K-mean
  • recognize deep generative models

Prerequisites

OR

  • Proven and solid skills and knowledge in statistics and Python programming

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