Machine Learning Fundamentals

Code: BIDA407
Apply this course towards: Business Intelligence and Data Analytics, Business Administration – Certificate, Business Administration – Diploma

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 our 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 under development. 

Learning objectives

Upon completion of this course, you will learn:

  • Data pre-processing steps, data exploring and visualization
  • Differentiate between supervised and unsupervised machine learning techniques
  • Optimization techniques (e.g., SGD)
  • Linear models and extensions to nonlinearity using kernel methods
  • Model complexity, overfitting and model regularization
  • Nonparametric models such as K-Nearest Neighbors (KNN)
  • Collaborative methods: boosting, bagging, random forests
  • Introduce Artificial Neural Networks with Sklearn, TensorFlow and Keras
  • Build Deep Models Using the Keras Sequential and Functional API
  • Applying pattern recognition and regression models with deep learning
  • Metrics and output evaluations for regression problems
  • Metrics (e.g., confusion matrix, AUC,…) and output evaluation/interpretation for classifications
  • Unsupervised methods: Dimensionality reduction, autoencoders and K-mean
  • Introduction to deep generative models

Prerequisites

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